{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "##### План предв.подготовки до разбиения/до цикла перекр.проверки.\n",
    "1) Загрузка данных.\n",
    "2) Удаление очевидных бесполезных переменных.\n",
    "3) Преобразование типов данных.\n",
    "4) Нормализация строковых значений и обработка дублирующих значений..\n",
    "5) Обработка редк.категорий, котор.можно выполнить до разбиения/перекр.проверки.\n",
    "6) Импутация пропусков, котор.можно выполнить до разбиения/перекр.проверки.\n",
    "7) Конструирование признаков, котор.можно выполнить до разбиения/перекр.проверки.\n",
    "\n",
    "##### План предв.подготовки после разбиения/до цикла перекр.проверки\n",
    "1) Преобразования максимизирующие нормальность, обработка выбросов.\n",
    "2) Обработка редк.категорий, котор.можно выполнить только после разбиения/внутри перекр.проверки.\n",
    "3) Импутация пропусков, котор.можно выполнить только после разбиения/внутри перекр.проверки.\n",
    "4) Конструирование признаков, котор.можно выполнить только после разбиения/внутри перекр.проверки.\n",
    "5) Стандартизация, дамми-кодирование."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем библиотеки numpy и pandas\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# импортируем функцию train_test_split(), с помощью\n",
    "# которой разбиваем данные на обучающие и тестовые\n",
    "from sklearn.model_selection import train_test_split\n",
    "# импортируем библиотеку matplotlib\n",
    "# для нашей функции plot_hist()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# импортируем модули norm и stats библиотеки scipy для\n",
    "# построения гистограмм и графиков квантиль-квантиль\n",
    "from scipy.stats import norm\n",
    "from scipy import stats\n",
    "# импортируем библиотеку seaborn для нашей \n",
    "# функции diagnostics_skewness()\n",
    "import seaborn as sns\n",
    "# импортируем класс PowerTransformer, позволяющий выполнить \n",
    "# преобразование Бокса-Кокса/Йео-Джонсона и стандартизацию,\n",
    "# и функцию cross_val_score для нашей функции importance_auc()\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# импортируем функцию roc_auc_score() для\n",
    "# вычисления AUC-ROC\n",
    "from sklearn.metrics import roc_auc_score\n",
    "# импортируем класс LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# импортируем классы BaseEstimator и TransformerMixin\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "import warnings\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import pygeohash as gh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cart_Number</th>\n",
       "      <th>Rooms_Number</th>\n",
       "      <th>Object_Type</th>\n",
       "      <th>Object_Type_ID</th>\n",
       "      <th>Settlement</th>\n",
       "      <th>Settlement_ID</th>\n",
       "      <th>District</th>\n",
       "      <th>District_Id</th>\n",
       "      <th>Street</th>\n",
       "      <th>Street_Id</th>\n",
       "      <th>House_Number</th>\n",
       "      <th>Metro</th>\n",
       "      <th>Metro_ID</th>\n",
       "      <th>Metro_m</th>\n",
       "      <th>Flats_Plan</th>\n",
       "      <th>Flats_Plan_ID</th>\n",
       "      <th>Stor</th>\n",
       "      <th>Storeys</th>\n",
       "      <th>Wall</th>\n",
       "      <th>Wall_ID</th>\n",
       "      <th>Space_Total</th>\n",
       "      <th>Space_Living</th>\n",
       "      <th>Space_Kitchen</th>\n",
       "      <th>Value_abs</th>\n",
       "      <th>Value_m</th>\n",
       "      <th>Balcon_Num</th>\n",
       "      <th>Sost</th>\n",
       "      <th>Sost_ID</th>\n",
       "      <th>Clozet</th>\n",
       "      <th>Clozet_ID</th>\n",
       "      <th>lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date_Create</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10083994</td>\n",
       "      <td>1</td>\n",
       "      <td>Типовая</td>\n",
       "      <td>2</td>\n",
       "      <td>Новосибирск</td>\n",
       "      <td>1</td>\n",
       "      <td>Первомайский</td>\n",
       "      <td>8</td>\n",
       "      <td>Одоевского</td>\n",
       "      <td>1143</td>\n",
       "      <td>1/11</td>\n",
       "      <td>Речной вокзал</td>\n",
       "      <td>291</td>\n",
       "      <td>16760</td>\n",
       "      <td>Студия</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>Монолит</td>\n",
       "      <td>3</td>\n",
       "      <td>22,00</td>\n",
       "      <td>12,00</td>\n",
       "      <td>0,00</td>\n",
       "      <td>1340000</td>\n",
       "      <td>60909,091</td>\n",
       "      <td>0</td>\n",
       "      <td>Отличное</td>\n",
       "      <td>6</td>\n",
       "      <td>Совмещенный</td>\n",
       "      <td>8</td>\n",
       "      <td>54,9371</td>\n",
       "      <td>83,1011</td>\n",
       "      <td>2014-04-21 11:24:39.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10015724</td>\n",
       "      <td>1</td>\n",
       "      <td>Улучшенной планировки</td>\n",
       "      <td>3</td>\n",
       "      <td>Новосибирск</td>\n",
       "      <td>1</td>\n",
       "      <td>Дзержинский</td>\n",
       "      <td>1</td>\n",
       "      <td>Адриена Лежена</td>\n",
       "      <td>964</td>\n",
       "      <td>23</td>\n",
       "      <td>Золотая Нива</td>\n",
       "      <td>284</td>\n",
       "      <td>683</td>\n",
       "      <td>Изолированная</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>Кирпич</td>\n",
       "      <td>2</td>\n",
       "      <td>42,70</td>\n",
       "      <td>22,00</td>\n",
       "      <td>11,80</td>\n",
       "      <td>3250000</td>\n",
       "      <td>76112,412</td>\n",
       "      <td>0</td>\n",
       "      <td>Хорошее</td>\n",
       "      <td>2</td>\n",
       "      <td>Совмещенный</td>\n",
       "      <td>8</td>\n",
       "      <td>55,0434</td>\n",
       "      <td>82,9822</td>\n",
       "      <td>2014-12-19 17:41:03.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10014604</td>\n",
       "      <td>1</td>\n",
       "      <td>Типовая</td>\n",
       "      <td>2</td>\n",
       "      <td>Новосибирск</td>\n",
       "      <td>1</td>\n",
       "      <td>Дзержинский</td>\n",
       "      <td>1</td>\n",
       "      <td>Бориса Богаткова</td>\n",
       "      <td>349</td>\n",
       "      <td>243</td>\n",
       "      <td>Золотая Нива</td>\n",
       "      <td>284</td>\n",
       "      <td>366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>Панель</td>\n",
       "      <td>1</td>\n",
       "      <td>29,90</td>\n",
       "      <td>17,20</td>\n",
       "      <td>5,60</td>\n",
       "      <td>2150000</td>\n",
       "      <td>71906,355</td>\n",
       "      <td>0</td>\n",
       "      <td>Удовлетворительное</td>\n",
       "      <td>7</td>\n",
       "      <td>Совмещенный</td>\n",
       "      <td>8</td>\n",
       "      <td>55,0405</td>\n",
       "      <td>82,9801</td>\n",
       "      <td>2013-04-26 09:48:05.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20094604</td>\n",
       "      <td>2</td>\n",
       "      <td>Типовая</td>\n",
       "      <td>2</td>\n",
       "      <td>Новосибирск</td>\n",
       "      <td>1</td>\n",
       "      <td>Советский</td>\n",
       "      <td>9</td>\n",
       "      <td>Лесосечная</td>\n",
       "      <td>980</td>\n",
       "      <td>4</td>\n",
       "      <td>Речной вокзал</td>\n",
       "      <td>291</td>\n",
       "      <td>21770</td>\n",
       "      <td>Изолированная</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>Панель</td>\n",
       "      <td>1</td>\n",
       "      <td>46,30</td>\n",
       "      <td>28,50</td>\n",
       "      <td>7,10</td>\n",
       "      <td>2300000</td>\n",
       "      <td>49676,026</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Раздельный</td>\n",
       "      <td>9</td>\n",
       "      <td>54,8902</td>\n",
       "      <td>83,0805</td>\n",
       "      <td>2017-07-26 12:16:17.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30075897</td>\n",
       "      <td>3</td>\n",
       "      <td>Улучшенной планировки</td>\n",
       "      <td>3</td>\n",
       "      <td>Новосибирск</td>\n",
       "      <td>1</td>\n",
       "      <td>Октябрьский</td>\n",
       "      <td>7</td>\n",
       "      <td>Гаранина</td>\n",
       "      <td>495</td>\n",
       "      <td>25/2</td>\n",
       "      <td>Золотая Нива</td>\n",
       "      <td>284</td>\n",
       "      <td>1150</td>\n",
       "      <td>Изолированная</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>Панель</td>\n",
       "      <td>1</td>\n",
       "      <td>66,00</td>\n",
       "      <td>42,00</td>\n",
       "      <td>9,00</td>\n",
       "      <td>2950000</td>\n",
       "      <td>44696,97</td>\n",
       "      <td>0</td>\n",
       "      <td>Хорошее</td>\n",
       "      <td>2</td>\n",
       "      <td>Раздельный</td>\n",
       "      <td>9</td>\n",
       "      <td>55,0288</td>\n",
       "      <td>82,9703</td>\n",
       "      <td>2016-06-06 15:35:46.470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cart_Number  Rooms_Number            Object_Type  Object_Type_ID  \\\n",
       "0     10083994             1                Типовая               2   \n",
       "1     10015724             1  Улучшенной планировки               3   \n",
       "2     10014604             1                Типовая               2   \n",
       "3     20094604             2                Типовая               2   \n",
       "4     30075897             3  Улучшенной планировки               3   \n",
       "\n",
       "    Settlement  Settlement_ID      District  District_Id            Street  \\\n",
       "0  Новосибирск              1  Первомайский            8        Одоевского   \n",
       "1  Новосибирск              1   Дзержинский            1    Адриена Лежена   \n",
       "2  Новосибирск              1   Дзержинский            1  Бориса Богаткова   \n",
       "3  Новосибирск              1     Советский            9        Лесосечная   \n",
       "4  Новосибирск              1   Октябрьский            7          Гаранина   \n",
       "\n",
       "   Street_Id House_Number          Metro  Metro_ID  Metro_m     Flats_Plan  \\\n",
       "0       1143         1/11  Речной вокзал       291    16760         Студия   \n",
       "1        964           23   Золотая Нива       284      683  Изолированная   \n",
       "2        349          243   Золотая Нива       284      366            NaN   \n",
       "3        980            4  Речной вокзал       291    21770  Изолированная   \n",
       "4        495         25/2   Золотая Нива       284     1150  Изолированная   \n",
       "\n",
       "   Flats_Plan_ID  Stor  Storeys     Wall  Wall_ID Space_Total Space_Living  \\\n",
       "0             23     4       18  Монолит        3       22,00        12,00   \n",
       "1              3     8       14   Кирпич        2       42,70        22,00   \n",
       "2              0     6        9   Панель        1       29,90        17,20   \n",
       "3              3     3        9   Панель        1       46,30        28,50   \n",
       "4              3     2       10   Панель        1       66,00        42,00   \n",
       "\n",
       "  Space_Kitchen  Value_abs    Value_m  Balcon_Num                Sost  \\\n",
       "0          0,00    1340000  60909,091           0            Отличное   \n",
       "1         11,80    3250000  76112,412           0             Хорошее   \n",
       "2          5,60    2150000  71906,355           0  Удовлетворительное   \n",
       "3          7,10    2300000  49676,026           1                 NaN   \n",
       "4          9,00    2950000   44696,97           0             Хорошее   \n",
       "\n",
       "   Sost_ID       Clozet  Clozet_ID      lat     Long              Date_Create  \n",
       "0        6  Совмещенный          8  54,9371  83,1011  2014-04-21 11:24:39.767  \n",
       "1        2  Совмещенный          8  55,0434  82,9822  2014-12-19 17:41:03.790  \n",
       "2        7  Совмещенный          8  55,0405  82,9801  2013-04-26 09:48:05.307  \n",
       "3        0   Раздельный          9  54,8902  83,0805  2017-07-26 12:16:17.380  \n",
       "4        2   Раздельный          9  55,0288  82,9703  2016-06-06 15:35:46.470  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1) Загрузка данных.\n",
    "\n",
    "# увеличиваем количество выводимых столбцов\n",
    "pd.set_option('display.max_columns', 35)\n",
    "\n",
    "data = pd.read_csv('flat_2.csv', sep=';')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34856 entries, 0 to 34855\n",
      "Data columns (total 33 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   Cart_Number     34856 non-null  int64 \n",
      " 1   Rooms_Number    34856 non-null  int64 \n",
      " 2   Object_Type     34856 non-null  object\n",
      " 3   Object_Type_ID  34856 non-null  int64 \n",
      " 4   Settlement      34856 non-null  object\n",
      " 5   Settlement_ID   34856 non-null  int64 \n",
      " 6   District        34856 non-null  object\n",
      " 7   District_Id     34856 non-null  int64 \n",
      " 8   Street          34856 non-null  object\n",
      " 9   Street_Id       34856 non-null  int64 \n",
      " 10  House_Number    34839 non-null  object\n",
      " 11  Metro           32952 non-null  object\n",
      " 12  Metro_ID        34856 non-null  int64 \n",
      " 13  Metro_m         34856 non-null  int64 \n",
      " 14  Flats_Plan      26866 non-null  object\n",
      " 15  Flats_Plan_ID   34856 non-null  int64 \n",
      " 16  Stor            34856 non-null  int64 \n",
      " 17  Storeys         34856 non-null  int64 \n",
      " 18  Wall            34303 non-null  object\n",
      " 19  Wall_ID         34856 non-null  int64 \n",
      " 20  Space_Total     34856 non-null  object\n",
      " 21  Space_Living    34856 non-null  object\n",
      " 22  Space_Kitchen   34856 non-null  object\n",
      " 23  Value_abs       34856 non-null  int64 \n",
      " 24  Value_m         34856 non-null  object\n",
      " 25  Balcon_Num      34856 non-null  int64 \n",
      " 26  Sost            32863 non-null  object\n",
      " 27  Sost_ID         34856 non-null  int64 \n",
      " 28  Clozet          32913 non-null  object\n",
      " 29  Clozet_ID       34856 non-null  int64 \n",
      " 30  lat             34856 non-null  object\n",
      " 31  Long            34856 non-null  object\n",
      " 32  Date_Create     34856 non-null  object\n",
      "dtypes: int64(16), object(17)\n",
      "memory usage: 8.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# смотрим типы переменных и информацию о количестве пропусков\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cart_Number          0\n",
      "Rooms_Number         0\n",
      "Object_Type          0\n",
      "Object_Type_ID       0\n",
      "Settlement           0\n",
      "Settlement_ID        0\n",
      "District             0\n",
      "District_Id          0\n",
      "Street               0\n",
      "Street_Id            0\n",
      "House_Number        17\n",
      "Metro             1904\n",
      "Metro_ID             0\n",
      "Metro_m              0\n",
      "Flats_Plan        7990\n",
      "Flats_Plan_ID        0\n",
      "Stor                 0\n",
      "Storeys              0\n",
      "Wall               553\n",
      "Wall_ID              0\n",
      "Space_Total          0\n",
      "Space_Living         0\n",
      "Space_Kitchen        0\n",
      "Value_abs            0\n",
      "Value_m              0\n",
      "Balcon_Num           0\n",
      "Sost              1993\n",
      "Sost_ID              0\n",
      "Clozet            1943\n",
      "Clozet_ID            0\n",
      "lat                  0\n",
      "Long                 0\n",
      "Date_Create          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# смотрим еще по другому наличие пропусков\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cart_Number</th>\n",
       "      <th>Rooms_Number</th>\n",
       "      <th>Object_Type_ID</th>\n",
       "      <th>Settlement_ID</th>\n",
       "      <th>District_Id</th>\n",
       "      <th>Street_Id</th>\n",
       "      <th>Metro_ID</th>\n",
       "      <th>Metro_m</th>\n",
       "      <th>Flats_Plan_ID</th>\n",
       "      <th>Stor</th>\n",
       "      <th>Storeys</th>\n",
       "      <th>Wall_ID</th>\n",
       "      <th>Value_abs</th>\n",
       "      <th>Balcon_Num</th>\n",
       "      <th>Sost_ID</th>\n",
       "      <th>Clozet_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38852364.878</td>\n",
       "      <td>1.759</td>\n",
       "      <td>2.968</td>\n",
       "      <td>44.367</td>\n",
       "      <td>7.079</td>\n",
       "      <td>1866.086</td>\n",
       "      <td>272.121</td>\n",
       "      <td>5809.013</td>\n",
       "      <td>4.282</td>\n",
       "      <td>5.312</td>\n",
       "      <td>9.375</td>\n",
       "      <td>2.440</td>\n",
       "      <td>2503426.877</td>\n",
       "      <td>0.428</td>\n",
       "      <td>4.235</td>\n",
       "      <td>7.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>50624528.994</td>\n",
       "      <td>0.825</td>\n",
       "      <td>3.853</td>\n",
       "      <td>236.387</td>\n",
       "      <td>7.150</td>\n",
       "      <td>2314.029</td>\n",
       "      <td>65.507</td>\n",
       "      <td>16192.766</td>\n",
       "      <td>6.650</td>\n",
       "      <td>3.916</td>\n",
       "      <td>5.012</td>\n",
       "      <td>8.025</td>\n",
       "      <td>1252328.568</td>\n",
       "      <td>0.523</td>\n",
       "      <td>2.677</td>\n",
       "      <td>2.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10386.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>610000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10075344.750</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>615.000</td>\n",
       "      <td>283.000</td>\n",
       "      <td>811.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1750000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>8.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20057076.500</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>1093.000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>3590.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2280000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>8.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>30074515.750</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>1608.000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>6870.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2921250.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>9.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>300711468.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>44.000</td>\n",
       "      <td>1673.000</td>\n",
       "      <td>64.000</td>\n",
       "      <td>12819.000</td>\n",
       "      <td>294.000</td>\n",
       "      <td>2500000.000</td>\n",
       "      <td>43.000</td>\n",
       "      <td>27.000</td>\n",
       "      <td>28.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>24000000.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>11.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cart_Number  Rooms_Number  Object_Type_ID  Settlement_ID  District_Id  \\\n",
       "count     34856.000     34856.000       34856.000      34856.000    34856.000   \n",
       "mean   38852364.878         1.759           2.968         44.367        7.079   \n",
       "std    50624528.994         0.825           3.853        236.387        7.150   \n",
       "min       10386.000         1.000           1.000          1.000        1.000   \n",
       "25%    10075344.750         1.000           2.000          1.000        4.000   \n",
       "50%    20057076.500         2.000           3.000          1.000        6.000   \n",
       "75%    30074515.750         2.000           3.000          1.000        7.000   \n",
       "max   300711468.000         4.000          44.000       1673.000       64.000   \n",
       "\n",
       "       Street_Id  Metro_ID     Metro_m  Flats_Plan_ID      Stor   Storeys  \\\n",
       "count  34856.000 34856.000   34856.000      34856.000 34856.000 34856.000   \n",
       "mean    1866.086   272.121    5809.013          4.282     5.312     9.375   \n",
       "std     2314.029    65.507   16192.766          6.650     3.916     5.012   \n",
       "min        6.000     0.000       0.000          0.000     1.000     0.000   \n",
       "25%      615.000   283.000     811.000          1.000     2.000     5.000   \n",
       "50%     1093.000   290.000    3590.000          3.000     4.000     9.000   \n",
       "75%     1608.000   290.000    6870.000          3.000     7.000    10.000   \n",
       "max    12819.000   294.000 2500000.000         43.000    27.000    28.000   \n",
       "\n",
       "        Wall_ID    Value_abs  Balcon_Num   Sost_ID  Clozet_ID  \n",
       "count 34856.000    34856.000   34856.000 34856.000  34856.000  \n",
       "mean      2.440  2503426.877       0.428     4.235      7.944  \n",
       "std       8.025  1252328.568       0.523     2.677      2.048  \n",
       "min       0.000   610000.000       0.000     0.000      0.000  \n",
       "25%       1.000  1750000.000       0.000     2.000      8.000  \n",
       "50%       2.000  2280000.000       0.000     4.000      8.000  \n",
       "75%       2.000  2921250.000       1.000     6.000      9.000  \n",
       "max      95.000 24000000.000       4.000    11.000     11.000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# смотрим статистики количественных переменных\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Удаление бесполезных переменных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# переменная - 'Date_Create' \n",
    "# это дата сделки, поэтому это временной ряд !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!11\n",
    "# НО ПОКА ЧТО ВРЕМЕННО!!!!!!! - Удаляем ее \n",
    "\n",
    "data.drop(['Date_Create'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Проверка на то, что все пропущенные значение имеют в своих соответствующих ID нулевое значение:\n",
    "\n",
    "# отключаем предупреждения\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print(data[data['Wall'].isnull()][['Wall','Wall_ID']][data['Wall_ID'] != 0].shape[0])\n",
    "print(data[data['Metro'].isnull()][['Metro','Metro_ID']][data['Metro_ID'] != 0].shape[0])\n",
    "print(data[data['Flats_Plan'].isnull()][['Flats_Plan','Flats_Plan_ID']][data['Flats_Plan_ID'] != 0].shape[0])\n",
    "print(data[data['Sost'].isnull()][['Sost','Sost_ID']][data['Sost_ID'] != 0].shape[0])\n",
    "print(data[data['Clozet'].isnull()][data['Clozet_ID'] != 0].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Проверка на то, что все ID-значения с нулевым значением соотетствуют соответствуют пропущенным значениям:\n",
    "\n",
    "print(data[data['Wall_ID'] == 0][data['Wall'].notnull()].shape[0])\n",
    "print(data[data['Metro_ID'] == 0][data['Metro'].notnull()].shape[0])\n",
    "print(data[data['Flats_Plan_ID'] == 0][data['Flats_Plan'].notnull()].shape[0])\n",
    "print(data[data['Sost_ID'] == 0][data['Sost'].notnull()].shape[0])\n",
    "print(data[data['Clozet_ID'] == 0][data['Clozet'].notnull()].shape[0])\n",
    "\n",
    "# включаем предупреждения\n",
    "warnings.filterwarnings(\"default\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rooms_Number', 'Object_Type_ID', 'Settlement_ID', 'District_Id',\n",
       "       'Street_Id', 'Metro_ID', 'Metro_m', 'Flats_Plan_ID', 'Stor', 'Storeys',\n",
       "       'Wall_ID', 'Space_Total', 'Space_Living', 'Space_Kitchen', 'Value_m',\n",
       "       'Balcon_Num', 'Sost_ID', 'Clozet_ID', 'lat', 'Long'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# В данном случае - удаляем все переменные у которых есть соответствующие им переменные с ID\n",
    "# и переменную общей стоимости площади 'Value_abs'\n",
    "\n",
    "data.drop(['Object_Type','Settlement','District','Street', 'House_Number', \n",
    "           'Metro','Flats_Plan','Wall','Sost','Clozet','Cart_Number','Value_abs'],axis=1, inplace=True)\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34856 entries, 0 to 34855\n",
      "Data columns (total 20 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   Rooms_Number    34856 non-null  int64 \n",
      " 1   Object_Type_ID  34856 non-null  int64 \n",
      " 2   Settlement_ID   34856 non-null  int64 \n",
      " 3   District_Id     34856 non-null  int64 \n",
      " 4   Street_Id       34856 non-null  int64 \n",
      " 5   Metro_ID        34856 non-null  int64 \n",
      " 6   Metro_m         34856 non-null  int64 \n",
      " 7   Flats_Plan_ID   34856 non-null  int64 \n",
      " 8   Stor            34856 non-null  int64 \n",
      " 9   Storeys         34856 non-null  int64 \n",
      " 10  Wall_ID         34856 non-null  int64 \n",
      " 11  Space_Total     34856 non-null  object\n",
      " 12  Space_Living    34856 non-null  object\n",
      " 13  Space_Kitchen   34856 non-null  object\n",
      " 14  Value_m         34856 non-null  object\n",
      " 15  Balcon_Num      34856 non-null  int64 \n",
      " 16  Sost_ID         34856 non-null  int64 \n",
      " 17  Clozet_ID       34856 non-null  int64 \n",
      " 18  lat             34856 non-null  object\n",
      " 19  Long            34856 non-null  object\n",
      "dtypes: int64(14), object(6)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# смотрим на типы переменных\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Space_Total</th>\n",
       "      <th>Space_Living</th>\n",
       "      <th>Space_Kitchen</th>\n",
       "      <th>Value_m</th>\n",
       "      <th>lat</th>\n",
       "      <th>Long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22,00</td>\n",
       "      <td>12,00</td>\n",
       "      <td>0,00</td>\n",
       "      <td>60909,091</td>\n",
       "      <td>54,9371</td>\n",
       "      <td>83,1011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42,70</td>\n",
       "      <td>22,00</td>\n",
       "      <td>11,80</td>\n",
       "      <td>76112,412</td>\n",
       "      <td>55,0434</td>\n",
       "      <td>82,9822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29,90</td>\n",
       "      <td>17,20</td>\n",
       "      <td>5,60</td>\n",
       "      <td>71906,355</td>\n",
       "      <td>55,0405</td>\n",
       "      <td>82,9801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46,30</td>\n",
       "      <td>28,50</td>\n",
       "      <td>7,10</td>\n",
       "      <td>49676,026</td>\n",
       "      <td>54,8902</td>\n",
       "      <td>83,0805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66,00</td>\n",
       "      <td>42,00</td>\n",
       "      <td>9,00</td>\n",
       "      <td>44696,97</td>\n",
       "      <td>55,0288</td>\n",
       "      <td>82,9703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Space_Total Space_Living Space_Kitchen    Value_m      lat     Long\n",
       "0       22,00        12,00          0,00  60909,091  54,9371  83,1011\n",
       "1       42,70        22,00         11,80  76112,412  55,0434  82,9822\n",
       "2       29,90        17,20          5,60  71906,355  55,0405  82,9801\n",
       "3       46,30        28,50          7,10  49676,026  54,8902  83,0805\n",
       "4       66,00        42,00          9,00   44696,97  55,0288  82,9703"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Смотрим на переменные имеющие тип object\n",
    "data[data.dtypes[data.dtypes == 'object'].index].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Видим что вместо точек стоят запятые в переменных 'Space_Total','Space_Living','Space_Kitchen', 'Value_m', 'lat','Long'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Преобразование типов данных.\n",
    "# 4) Нормализация строковых значений и обработка дублирующих значений.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# заменяем запятые на точки и преобразуем в тип float\n",
    "for i in ['Space_Total','Space_Living','Space_Kitchen', 'Value_m', 'lat','Long']:\n",
    "    data[i] = data[i].str.replace(',','.').astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Space_Total</th>\n",
       "      <th>Space_Living</th>\n",
       "      <th>Space_Kitchen</th>\n",
       "      <th>Value_m</th>\n",
       "      <th>lat</th>\n",
       "      <th>Long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>60909.091</td>\n",
       "      <td>54.937</td>\n",
       "      <td>83.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.700</td>\n",
       "      <td>22.000</td>\n",
       "      <td>11.800</td>\n",
       "      <td>76112.412</td>\n",
       "      <td>55.043</td>\n",
       "      <td>82.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.900</td>\n",
       "      <td>17.200</td>\n",
       "      <td>5.600</td>\n",
       "      <td>71906.355</td>\n",
       "      <td>55.041</td>\n",
       "      <td>82.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.300</td>\n",
       "      <td>28.500</td>\n",
       "      <td>7.100</td>\n",
       "      <td>49676.026</td>\n",
       "      <td>54.890</td>\n",
       "      <td>83.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>44696.970</td>\n",
       "      <td>55.029</td>\n",
       "      <td>82.970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Space_Total  Space_Living  Space_Kitchen   Value_m    lat   Long\n",
       "0       22.000        12.000          0.000 60909.091 54.937 83.101\n",
       "1       42.700        22.000         11.800 76112.412 55.043 82.982\n",
       "2       29.900        17.200          5.600 71906.355 55.041 82.980\n",
       "3       46.300        28.500          7.100 49676.026 54.890 83.081\n",
       "4       66.000        42.000          9.000 44696.970 55.029 82.970"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# смотрим на преобразованные данные в этих столбцах\n",
    "data[[ 'Space_Total', 'Space_Living',\n",
    "       'Space_Kitchen', 'Value_m', 'lat',\n",
    "       'Long']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34856 entries, 0 to 34855\n",
      "Data columns (total 20 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Rooms_Number    34856 non-null  int64  \n",
      " 1   Object_Type_ID  34856 non-null  int64  \n",
      " 2   Settlement_ID   34856 non-null  int64  \n",
      " 3   District_Id     34856 non-null  int64  \n",
      " 4   Street_Id       34856 non-null  int64  \n",
      " 5   Metro_ID        34856 non-null  int64  \n",
      " 6   Metro_m         34856 non-null  int64  \n",
      " 7   Flats_Plan_ID   34856 non-null  int64  \n",
      " 8   Stor            34856 non-null  int64  \n",
      " 9   Storeys         34856 non-null  int64  \n",
      " 10  Wall_ID         34856 non-null  int64  \n",
      " 11  Space_Total     34856 non-null  float64\n",
      " 12  Space_Living    34856 non-null  float64\n",
      " 13  Space_Kitchen   34856 non-null  float64\n",
      " 14  Value_m         34856 non-null  float64\n",
      " 15  Balcon_Num      34856 non-null  int64  \n",
      " 16  Sost_ID         34856 non-null  int64  \n",
      " 17  Clozet_ID       34856 non-null  int64  \n",
      " 18  lat             34856 non-null  float64\n",
      " 19  Long            34856 non-null  float64\n",
      "dtypes: float64(6), int64(14)\n",
      "memory usage: 5.3 MB\n"
     ]
    }
   ],
   "source": [
    "# смотрим на типы переменных опять\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms_Number</th>\n",
       "      <th>Object_Type_ID</th>\n",
       "      <th>Settlement_ID</th>\n",
       "      <th>District_Id</th>\n",
       "      <th>Street_Id</th>\n",
       "      <th>Metro_ID</th>\n",
       "      <th>Metro_m</th>\n",
       "      <th>Flats_Plan_ID</th>\n",
       "      <th>Stor</th>\n",
       "      <th>Storeys</th>\n",
       "      <th>Wall_ID</th>\n",
       "      <th>Balcon_Num</th>\n",
       "      <th>Sost_ID</th>\n",
       "      <th>Clozet_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>16760</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>964</td>\n",
       "      <td>284</td>\n",
       "      <td>683</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>284</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>980</td>\n",
       "      <td>291</td>\n",
       "      <td>21770</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>495</td>\n",
       "      <td>284</td>\n",
       "      <td>1150</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms_Number  Object_Type_ID  Settlement_ID  District_Id  Street_Id  \\\n",
       "0             1               2              1            8       1143   \n",
       "1             1               3              1            1        964   \n",
       "2             1               2              1            1        349   \n",
       "3             2               2              1            9        980   \n",
       "4             3               3              1            7        495   \n",
       "\n",
       "   Metro_ID  Metro_m  Flats_Plan_ID  Stor  Storeys  Wall_ID  Balcon_Num  \\\n",
       "0       291    16760             23     4       18        3           0   \n",
       "1       284      683              3     8       14        2           0   \n",
       "2       284      366              0     6        9        1           0   \n",
       "3       291    21770              3     3        9        1           1   \n",
       "4       284     1150              3     2       10        1           0   \n",
       "\n",
       "   Sost_ID  Clozet_ID  \n",
       "0        6          8  \n",
       "1        2          8  \n",
       "2        7          8  \n",
       "3        0          9  \n",
       "4        2          9  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Смотрим на переменные имеющие тип int\n",
    "data[data.dtypes[data.dtypes == 'int64'].index].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Metro_m           2585\n",
       "Street_Id          913\n",
       "Settlement_ID       87\n",
       "Storeys             29\n",
       "Stor                27\n",
       "Object_Type_ID      26\n",
       "District_Id         24\n",
       "Wall_ID             18\n",
       "Flats_Plan_ID       18\n",
       "Metro_ID            16\n",
       "Clozet_ID           12\n",
       "Sost_ID              8\n",
       "Balcon_Num           5\n",
       "Rooms_Number         4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Смотрим на количество уникальных значений для этих столбцов\n",
    "data[data.dtypes[data.dtypes == 'int64'].index].nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6) Импутация пропусков, котор.можно выполнить до разбиения/перекр.проверки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rooms_Number      0\n",
      "Object_Type_ID    0\n",
      "Settlement_ID     0\n",
      "District_Id       0\n",
      "Street_Id         0\n",
      "Metro_ID          0\n",
      "Metro_m           0\n",
      "Flats_Plan_ID     0\n",
      "Stor              0\n",
      "Storeys           0\n",
      "Wall_ID           0\n",
      "Space_Total       0\n",
      "Space_Living      0\n",
      "Space_Kitchen     0\n",
      "Value_m           0\n",
      "Balcon_Num        0\n",
      "Sost_ID           0\n",
      "Clozet_ID         0\n",
      "lat               0\n",
      "Long              0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# проверяем наличие пропусков\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Получается что в данном варианте - пропусков нет\n",
    "# а точнее, те что есть обозначены нулевым значением\n",
    "с этим нужно будет поработать дальше и возможно подобрать нужную импутацию\n",
    "на стадии после разбиения/перекрестной проверке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms_Number</th>\n",
       "      <th>Object_Type_ID</th>\n",
       "      <th>Settlement_ID</th>\n",
       "      <th>District_Id</th>\n",
       "      <th>Street_Id</th>\n",
       "      <th>Metro_ID</th>\n",
       "      <th>Metro_m</th>\n",
       "      <th>Flats_Plan_ID</th>\n",
       "      <th>Stor</th>\n",
       "      <th>Storeys</th>\n",
       "      <th>Wall_ID</th>\n",
       "      <th>Space_Total</th>\n",
       "      <th>Space_Living</th>\n",
       "      <th>Space_Kitchen</th>\n",
       "      <th>Value_m</th>\n",
       "      <th>Balcon_Num</th>\n",
       "      <th>Sost_ID</th>\n",
       "      <th>Clozet_ID</th>\n",
       "      <th>lat</th>\n",
       "      <th>Long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.759</td>\n",
       "      <td>2.968</td>\n",
       "      <td>44.367</td>\n",
       "      <td>7.079</td>\n",
       "      <td>1866.086</td>\n",
       "      <td>272.121</td>\n",
       "      <td>5809.013</td>\n",
       "      <td>4.282</td>\n",
       "      <td>5.312</td>\n",
       "      <td>9.375</td>\n",
       "      <td>2.440</td>\n",
       "      <td>45.638</td>\n",
       "      <td>26.007</td>\n",
       "      <td>7.223</td>\n",
       "      <td>55475.020</td>\n",
       "      <td>0.428</td>\n",
       "      <td>4.235</td>\n",
       "      <td>7.944</td>\n",
       "      <td>54.973</td>\n",
       "      <td>82.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.825</td>\n",
       "      <td>3.853</td>\n",
       "      <td>236.387</td>\n",
       "      <td>7.150</td>\n",
       "      <td>2314.029</td>\n",
       "      <td>65.507</td>\n",
       "      <td>16192.766</td>\n",
       "      <td>6.650</td>\n",
       "      <td>3.916</td>\n",
       "      <td>5.012</td>\n",
       "      <td>8.025</td>\n",
       "      <td>25.280</td>\n",
       "      <td>16.392</td>\n",
       "      <td>4.419</td>\n",
       "      <td>14442.254</td>\n",
       "      <td>0.523</td>\n",
       "      <td>2.677</td>\n",
       "      <td>2.048</td>\n",
       "      <td>1.181</td>\n",
       "      <td>1.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>615.000</td>\n",
       "      <td>283.000</td>\n",
       "      <td>811.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>17.000</td>\n",
       "      <td>5.700</td>\n",
       "      <td>46210.721</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>54.964</td>\n",
       "      <td>82.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>1093.000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>3590.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43.000</td>\n",
       "      <td>25.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>54054.054</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>55.005</td>\n",
       "      <td>82.945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>1608.000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>6870.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>56.500</td>\n",
       "      <td>33.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>63636.364</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>55.047</td>\n",
       "      <td>82.993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000</td>\n",
       "      <td>44.000</td>\n",
       "      <td>1673.000</td>\n",
       "      <td>64.000</td>\n",
       "      <td>12819.000</td>\n",
       "      <td>294.000</td>\n",
       "      <td>2500000.000</td>\n",
       "      <td>43.000</td>\n",
       "      <td>27.000</td>\n",
       "      <td>28.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>3217.000</td>\n",
       "      <td>1806.000</td>\n",
       "      <td>101.000</td>\n",
       "      <td>544512.195</td>\n",
       "      <td>4.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>57.193</td>\n",
       "      <td>84.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rooms_Number  Object_Type_ID  Settlement_ID  District_Id  Street_Id  \\\n",
       "count     34856.000       34856.000      34856.000    34856.000  34856.000   \n",
       "mean          1.759           2.968         44.367        7.079   1866.086   \n",
       "std           0.825           3.853        236.387        7.150   2314.029   \n",
       "min           1.000           1.000          1.000        1.000      6.000   \n",
       "25%           1.000           2.000          1.000        4.000    615.000   \n",
       "50%           2.000           3.000          1.000        6.000   1093.000   \n",
       "75%           2.000           3.000          1.000        7.000   1608.000   \n",
       "max           4.000          44.000       1673.000       64.000  12819.000   \n",
       "\n",
       "       Metro_ID     Metro_m  Flats_Plan_ID      Stor   Storeys   Wall_ID  \\\n",
       "count 34856.000   34856.000      34856.000 34856.000 34856.000 34856.000   \n",
       "mean    272.121    5809.013          4.282     5.312     9.375     2.440   \n",
       "std      65.507   16192.766          6.650     3.916     5.012     8.025   \n",
       "min       0.000       0.000          0.000     1.000     0.000     0.000   \n",
       "25%     283.000     811.000          1.000     2.000     5.000     1.000   \n",
       "50%     290.000    3590.000          3.000     4.000     9.000     2.000   \n",
       "75%     290.000    6870.000          3.000     7.000    10.000     2.000   \n",
       "max     294.000 2500000.000         43.000    27.000    28.000    95.000   \n",
       "\n",
       "       Space_Total  Space_Living  Space_Kitchen    Value_m  Balcon_Num  \\\n",
       "count    34856.000     34856.000      34856.000  34856.000   34856.000   \n",
       "mean        45.638        26.007          7.223  55475.020       0.428   \n",
       "std         25.280        16.392          4.419  14442.254       0.523   \n",
       "min          0.000         0.000          0.000      0.000       0.000   \n",
       "25%         32.000        17.000          5.700  46210.721       0.000   \n",
       "50%         43.000        25.000          7.000  54054.054       0.000   \n",
       "75%         56.500        33.000          9.000  63636.364       1.000   \n",
       "max       3217.000      1806.000        101.000 544512.195       4.000   \n",
       "\n",
       "        Sost_ID  Clozet_ID       lat      Long  \n",
       "count 34856.000  34856.000 34856.000 34856.000  \n",
       "mean      4.235      7.944    54.973    82.911  \n",
       "std       2.677      2.048     1.181     1.795  \n",
       "min       0.000      0.000     0.000     0.000  \n",
       "25%       2.000      8.000    54.964    82.899  \n",
       "50%       4.000      8.000    55.005    82.945  \n",
       "75%       6.000      9.000    55.047    82.993  \n",
       "max      11.000     11.000    57.193    84.422  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# смотрим статистики количественных переменных\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Видны большие выбросы по переменным - Metro_m, \n",
    "# Space_Total,Space_Living,Space_Kitchen.\n",
    "# Так же есть в зависимой переменной Value_m какие то нулевые значение(что вроде бы не может быть?) \n",
    "# и подозрительно резко  большие(до 544512.195 !!! )\n",
    "\n",
    "# Видно так-же что переменная Metro_m имеет сильно отличающиеся медиану и среднее, \n",
    "# что указывает на не нормализованное распределение. \n",
    "\n",
    "# Возможно большие выбросы по переменным Space_Total,Space_Living,Space_Kitchen.\n",
    "# указываюь на редкое элитное жилье, типа особняков с огромными площадями."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17337    2500000\n",
       "9654     1000000\n",
       "13511     290000\n",
       "13512     290000\n",
       "13906     100000\n",
       "17105      75000\n",
       "7325       61800\n",
       "14420      61800\n",
       "19894      61800\n",
       "27444      61800\n",
       "27474      61800\n",
       "20767      60163\n",
       "5965       60020\n",
       "1442       60000\n",
       "1443       60000\n",
       "4102       60000\n",
       "4618       60000\n",
       "6047       60000\n",
       "7833       60000\n",
       "8756       60000\n",
       "10545      60000\n",
       "11450      60000\n",
       "12299      60000\n",
       "12620      60000\n",
       "13978      60000\n",
       "14289      60000\n",
       "18886      60000\n",
       "19980      60000\n",
       "20725      60000\n",
       "22115      60000\n",
       "Name: Metro_m, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выведем наибольшие значений Metro_m\n",
    "data['Metro_m'].nlargest(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance_threshold = 15000"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# взглянем на значения Metro_m больше эпределенного порога distance_threshold\n",
    "# зададим для начала distance_threshold=15000\n",
    "# но в дальнейшем это значение можно подобрать как гиперпараметр\n",
    "\n",
    "data[data['Metro_m'] > distance_threshold ]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "возможно стоит заменить все значения переменной Metro_m\n",
    "выше определенного порога на одно значение\n",
    "означающее отсутствие метро.\n",
    "В дальнейшем можно подобрать уровень этого порога как гиперпараметр."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# взглянем на значения Metro_m менее 1\n",
    "data[data['Metro_m'] < 1]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Получается что все наблюдения у которых в столбце Metro_m стоит нулевое значение\n",
    "имеют в столбце Metro_ID значение 0\n",
    "Т.е., видимо, считается что рядом у них рядом метро нет,\n",
    "иначе говоря - они тоже на большом удалении от метро\n",
    "и их можно попробовать обьеденить в отдельную категорию с наблюдениями у которых значение \n",
    "Metro_m выше определенного порога."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# все значения переменной Metro_ID\n",
    "# больше distance_threshold пометим как отсутствие метро\n",
    "data['Metro_m'] = np.where(data['Metro_m'] > distance_threshold, 0, data['Metro_m'])\n",
    "data['Metro_ID'] = np.where(data['Metro_m'] > distance_threshold,0, data['Metro_ID'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# проверяем наличие нулевых значений\n",
    "print((data == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) Обработка редк.категорий, котор.можно выполнить до разбиения/перекр.проверки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    16202\n",
      "2    11697\n",
      "3     6103\n",
      "4      854\n",
      "Name: Rooms_Number, dtype: int64\n",
      "3     17389\n",
      "1      7481\n",
      "2      6645\n",
      "4      1431\n",
      "7       818\n",
      "5       548\n",
      "42      222\n",
      "17      127\n",
      "29       81\n",
      "27       44\n",
      "25       11\n",
      "6        10\n",
      "14        9\n",
      "28        7\n",
      "20        6\n",
      "12        6\n",
      "24        6\n",
      "21        4\n",
      "16        3\n",
      "31        2\n",
      "8         1\n",
      "9         1\n",
      "44        1\n",
      "19        1\n",
      "22        1\n",
      "13        1\n",
      "Name: Object_Type_ID, dtype: int64\n",
      "1       32103\n",
      "2         987\n",
      "3         251\n",
      "291       244\n",
      "42        233\n",
      "        ...  \n",
      "1573        1\n",
      "1483        1\n",
      "1530        1\n",
      "1532        1\n",
      "1535        1\n",
      "Name: Settlement_ID, Length: 87, dtype: int64\n",
      "6     5717\n",
      "7     5430\n",
      "5     4626\n",
      "4     3579\n",
      "1     3199\n",
      "3     3013\n",
      "8     2373\n",
      "10    1590\n",
      "9     1417\n",
      "33    1181\n",
      "2     1158\n",
      "15     988\n",
      "19     285\n",
      "50     185\n",
      "57      54\n",
      "51      18\n",
      "48      15\n",
      "59      12\n",
      "55      10\n",
      "60       2\n",
      "52       1\n",
      "56       1\n",
      "58       1\n",
      "64       1\n",
      "Name: District_Id, dtype: int64\n",
      "693      835\n",
      "1143     808\n",
      "8054     799\n",
      "485      786\n",
      "1238     774\n",
      "        ... \n",
      "12675      1\n",
      "9939       1\n",
      "1815       1\n",
      "6069       1\n",
      "8172       1\n",
      "Name: Street_Id, Length: 913, dtype: int64\n",
      "290    10135\n",
      "283     5715\n",
      "291     4867\n",
      "284     4242\n",
      "0       1904\n",
      "287     1507\n",
      "294     1486\n",
      "293     1273\n",
      "282     1127\n",
      "289      746\n",
      "288      700\n",
      "286      653\n",
      "285      356\n",
      "292      108\n",
      "281       21\n",
      "280       16\n",
      "Name: Metro_ID, dtype: int64\n",
      "0       6144\n",
      "4500     250\n",
      "2000     203\n",
      "1000     188\n",
      "4000     173\n",
      "        ... \n",
      "674        1\n",
      "337        1\n",
      "578        1\n",
      "9500       1\n",
      "268        1\n",
      "Name: Metro_m, Length: 1926, dtype: int64\n",
      "3     17785\n",
      "0      7990\n",
      "23     3734\n",
      "2      2783\n",
      "1      2174\n",
      "4       354\n",
      "30        9\n",
      "43        5\n",
      "7         5\n",
      "28        3\n",
      "5         3\n",
      "13        2\n",
      "25        2\n",
      "33        2\n",
      "27        2\n",
      "11        1\n",
      "6         1\n",
      "26        1\n",
      "Name: Flats_Plan_ID, dtype: int64\n",
      "2     4944\n",
      "3     4616\n",
      "1     4315\n",
      "4     4230\n",
      "5     4230\n",
      "9     2085\n",
      "6     2071\n",
      "7     2003\n",
      "8     1984\n",
      "10    1117\n",
      "12     539\n",
      "14     476\n",
      "11     468\n",
      "13     456\n",
      "15     395\n",
      "16     363\n",
      "17     245\n",
      "18      59\n",
      "19      53\n",
      "20      47\n",
      "22      40\n",
      "23      36\n",
      "24      34\n",
      "21      28\n",
      "25      19\n",
      "27       2\n",
      "26       1\n",
      "Name: Stor, dtype: int64\n",
      "5     9946\n",
      "9     7461\n",
      "10    6371\n",
      "17    3202\n",
      "16     917\n",
      "3      856\n",
      "2      839\n",
      "18     803\n",
      "4      761\n",
      "12     536\n",
      "6      399\n",
      "14     382\n",
      "25     364\n",
      "11     355\n",
      "19     345\n",
      "7      208\n",
      "26     184\n",
      "13     179\n",
      "15     160\n",
      "24     121\n",
      "27     114\n",
      "22      94\n",
      "1       92\n",
      "8       72\n",
      "20      42\n",
      "21      38\n",
      "23      11\n",
      "0        3\n",
      "28       1\n",
      "Name: Storeys, dtype: int64\n",
      "1     15370\n",
      "2     15169\n",
      "3      2942\n",
      "0       553\n",
      "83      230\n",
      "6       210\n",
      "4       153\n",
      "7        93\n",
      "94       40\n",
      "95       38\n",
      "41       21\n",
      "9        14\n",
      "11       10\n",
      "36        5\n",
      "42        3\n",
      "12        2\n",
      "24        2\n",
      "57        1\n",
      "Name: Wall_ID, dtype: int64\n",
      "0    20395\n",
      "1    14015\n",
      "2      424\n",
      "3       19\n",
      "4        3\n",
      "Name: Balcon_Num, dtype: int64\n",
      "2     14737\n",
      "6      7948\n",
      "7      3571\n",
      "9      2757\n",
      "0      1993\n",
      "4      1856\n",
      "5      1453\n",
      "11      541\n",
      "Name: Sost_ID, dtype: int64\n",
      "8     18557\n",
      "9     13363\n",
      "0      1943\n",
      "10      704\n",
      "3       133\n",
      "6        37\n",
      "7        33\n",
      "2        24\n",
      "5        22\n",
      "4        19\n",
      "1        19\n",
      "11        2\n",
      "Name: Clozet_ID, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# смотрим частоты  переменных c типом 'int64' \n",
    "for col in data.dtypes[data.dtypes == 'int64'].index:\n",
    "    print(data[col].value_counts(dropna=False))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# Видим редкие категории, в таких переменных как :\n",
    "# Object_Type,Metro,Flats_Plan,Wall,Clozet\n",
    "\n",
    "# Возможно, переменную 'Clozet' стоит оставим на потом, \n",
    "# так как отсутствие туалета может указывать на  нежилые помещения или малокомфортные помещения \n",
    "\n",
    "# укрупняем редкие категории\n",
    "for col in ['Object_Type_ID','Metro_ID','Flats_Plan_ID','Wall_ID','Clozet_ID']:\n",
    "    data.loc[data[col].value_counts()[data[col]].values < 39, col] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    16202\n",
      "2    11697\n",
      "3     6103\n",
      "4      854\n",
      "Name: Rooms_Number, dtype: int64\n",
      "3     17389\n",
      "1      7481\n",
      "2      6645\n",
      "4      1431\n",
      "7       818\n",
      "5       548\n",
      "42      222\n",
      "17      127\n",
      "29       81\n",
      "27       44\n",
      "25       11\n",
      "6        10\n",
      "14        9\n",
      "28        7\n",
      "20        6\n",
      "12        6\n",
      "24        6\n",
      "21        4\n",
      "16        3\n",
      "31        2\n",
      "8         1\n",
      "9         1\n",
      "44        1\n",
      "19        1\n",
      "22        1\n",
      "13        1\n",
      "Name: Object_Type_ID, dtype: int64\n",
      "1       32103\n",
      "2         987\n",
      "3         251\n",
      "291       244\n",
      "42        233\n",
      "        ...  \n",
      "1573        1\n",
      "1483        1\n",
      "1530        1\n",
      "1532        1\n",
      "1535        1\n",
      "Name: Settlement_ID, Length: 87, dtype: int64\n",
      "6     5717\n",
      "7     5430\n",
      "5     4626\n",
      "4     3579\n",
      "1     3199\n",
      "3     3013\n",
      "8     2373\n",
      "10    1590\n",
      "9     1417\n",
      "33    1181\n",
      "2     1158\n",
      "15     988\n",
      "19     285\n",
      "50     185\n",
      "57      54\n",
      "51      18\n",
      "48      15\n",
      "59      12\n",
      "55      10\n",
      "60       2\n",
      "52       1\n",
      "56       1\n",
      "58       1\n",
      "64       1\n",
      "Name: District_Id, dtype: int64\n",
      "693      835\n",
      "1143     808\n",
      "8054     799\n",
      "485      786\n",
      "1238     774\n",
      "        ... \n",
      "12675      1\n",
      "9939       1\n",
      "1815       1\n",
      "6069       1\n",
      "8172       1\n",
      "Name: Street_Id, Length: 913, dtype: int64\n",
      "290    10135\n",
      "283     5715\n",
      "291     4867\n",
      "284     4242\n",
      "0       1904\n",
      "287     1507\n",
      "294     1486\n",
      "293     1273\n",
      "282     1127\n",
      "289      746\n",
      "288      700\n",
      "286      653\n",
      "285      356\n",
      "292      108\n",
      "281       21\n",
      "280       16\n",
      "Name: Metro_ID, dtype: int64\n",
      "0       6144\n",
      "4500     250\n",
      "2000     203\n",
      "1000     188\n",
      "4000     173\n",
      "        ... \n",
      "674        1\n",
      "337        1\n",
      "578        1\n",
      "9500       1\n",
      "268        1\n",
      "Name: Metro_m, Length: 1926, dtype: int64\n",
      "3     17785\n",
      "0      7990\n",
      "23     3734\n",
      "2      2783\n",
      "1      2174\n",
      "4       354\n",
      "30        9\n",
      "43        5\n",
      "7         5\n",
      "28        3\n",
      "5         3\n",
      "13        2\n",
      "25        2\n",
      "33        2\n",
      "27        2\n",
      "11        1\n",
      "6         1\n",
      "26        1\n",
      "Name: Flats_Plan_ID, dtype: int64\n",
      "2     4944\n",
      "3     4616\n",
      "1     4315\n",
      "4     4230\n",
      "5     4230\n",
      "9     2085\n",
      "6     2071\n",
      "7     2003\n",
      "8     1984\n",
      "10    1117\n",
      "12     539\n",
      "14     476\n",
      "11     468\n",
      "13     456\n",
      "15     395\n",
      "16     363\n",
      "17     245\n",
      "18      59\n",
      "19      53\n",
      "20      47\n",
      "22      40\n",
      "23      36\n",
      "24      34\n",
      "21      28\n",
      "25      19\n",
      "27       2\n",
      "26       1\n",
      "Name: Stor, dtype: int64\n",
      "5     9946\n",
      "9     7461\n",
      "10    6371\n",
      "17    3202\n",
      "16     917\n",
      "3      856\n",
      "2      839\n",
      "18     803\n",
      "4      761\n",
      "12     536\n",
      "6      399\n",
      "14     382\n",
      "25     364\n",
      "11     355\n",
      "19     345\n",
      "7      208\n",
      "26     184\n",
      "13     179\n",
      "15     160\n",
      "24     121\n",
      "27     114\n",
      "22      94\n",
      "1       92\n",
      "8       72\n",
      "20      42\n",
      "21      38\n",
      "23      11\n",
      "0        3\n",
      "28       1\n",
      "Name: Storeys, dtype: int64\n",
      "1     15370\n",
      "2     15169\n",
      "3      2942\n",
      "0       553\n",
      "83      230\n",
      "6       210\n",
      "4       153\n",
      "7        93\n",
      "94       40\n",
      "95       38\n",
      "41       21\n",
      "9        14\n",
      "11       10\n",
      "36        5\n",
      "42        3\n",
      "12        2\n",
      "24        2\n",
      "57        1\n",
      "Name: Wall_ID, dtype: int64\n",
      "0    20395\n",
      "1    14015\n",
      "2      424\n",
      "3       19\n",
      "4        3\n",
      "Name: Balcon_Num, dtype: int64\n",
      "2     14737\n",
      "6      7948\n",
      "7      3571\n",
      "9      2757\n",
      "0      1993\n",
      "4      1856\n",
      "5      1453\n",
      "11      541\n",
      "Name: Sost_ID, dtype: int64\n",
      "8     18557\n",
      "9     13363\n",
      "0      1943\n",
      "10      704\n",
      "3       133\n",
      "6        37\n",
      "7        33\n",
      "2        24\n",
      "5        22\n",
      "4        19\n",
      "1        19\n",
      "11        2\n",
      "Name: Clozet_ID, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# смотрим снова частоты категорий ( переменных c типом 'int64' )\n",
    "for col in data.dtypes[data.dtypes == 'int64'].index:\n",
    "    print(data[col].value_counts(dropna=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms_Number</th>\n",
       "      <th>Object_Type_ID</th>\n",
       "      <th>Settlement_ID</th>\n",
       "      <th>District_Id</th>\n",
       "      <th>Street_Id</th>\n",
       "      <th>Metro_ID</th>\n",
       "      <th>Metro_m</th>\n",
       "      <th>Flats_Plan_ID</th>\n",
       "      <th>Stor</th>\n",
       "      <th>Storeys</th>\n",
       "      <th>Wall_ID</th>\n",
       "      <th>Space_Total</th>\n",
       "      <th>Space_Living</th>\n",
       "      <th>Space_Kitchen</th>\n",
       "      <th>Value_m</th>\n",
       "      <th>Balcon_Num</th>\n",
       "      <th>Sost_ID</th>\n",
       "      <th>Clozet_ID</th>\n",
       "      <th>lat</th>\n",
       "      <th>Long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>22.000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>60909.091</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>54.937</td>\n",
       "      <td>83.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>964</td>\n",
       "      <td>284</td>\n",
       "      <td>683</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>42.700</td>\n",
       "      <td>22.000</td>\n",
       "      <td>11.800</td>\n",
       "      <td>76112.412</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>55.043</td>\n",
       "      <td>82.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>284</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>29.900</td>\n",
       "      <td>17.200</td>\n",
       "      <td>5.600</td>\n",
       "      <td>71906.355</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>55.041</td>\n",
       "      <td>82.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>980</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>46.300</td>\n",
       "      <td>28.500</td>\n",
       "      <td>7.100</td>\n",
       "      <td>49676.026</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>54.890</td>\n",
       "      <td>83.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>495</td>\n",
       "      <td>284</td>\n",
       "      <td>1150</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>66.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>44696.970</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>55.029</td>\n",
       "      <td>82.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>980</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>59.000</td>\n",
       "      <td>38.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>47457.627</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>54.890</td>\n",
       "      <td>83.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1835</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>47.000</td>\n",
       "      <td>31.200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>67659.574</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>54.888</td>\n",
       "      <td>83.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1835</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>64.000</td>\n",
       "      <td>39.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>59375.000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>54.889</td>\n",
       "      <td>83.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>348</td>\n",
       "      <td>284</td>\n",
       "      <td>682</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>46.000</td>\n",
       "      <td>27.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>65217.391</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>55.033</td>\n",
       "      <td>82.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>348</td>\n",
       "      <td>284</td>\n",
       "      <td>568</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>46.600</td>\n",
       "      <td>28.600</td>\n",
       "      <td>7.200</td>\n",
       "      <td>60085.837</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>55.034</td>\n",
       "      <td>82.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>21.800</td>\n",
       "      <td>12.700</td>\n",
       "      <td>3.200</td>\n",
       "      <td>59633.028</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>54.938</td>\n",
       "      <td>83.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>21.900</td>\n",
       "      <td>12.200</td>\n",
       "      <td>3.300</td>\n",
       "      <td>55936.073</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>54.938</td>\n",
       "      <td>83.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>25.600</td>\n",
       "      <td>13.900</td>\n",
       "      <td>6.800</td>\n",
       "      <td>70703.125</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>54.938</td>\n",
       "      <td>83.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>29.000</td>\n",
       "      <td>13.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>56896.552</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>54.938</td>\n",
       "      <td>83.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>36.200</td>\n",
       "      <td>18.000</td>\n",
       "      <td>13.000</td>\n",
       "      <td>45580.110</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>54.938</td>\n",
       "      <td>83.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>23.400</td>\n",
       "      <td>15.600</td>\n",
       "      <td>0.000</td>\n",
       "      <td>44230.769</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>54.937</td>\n",
       "      <td>83.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>23.300</td>\n",
       "      <td>14.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>54506.438</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>54.938</td>\n",
       "      <td>83.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>348</td>\n",
       "      <td>284</td>\n",
       "      <td>471</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>34.200</td>\n",
       "      <td>18.400</td>\n",
       "      <td>6.300</td>\n",
       "      <td>54093.567</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>55.036</td>\n",
       "      <td>82.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>348</td>\n",
       "      <td>284</td>\n",
       "      <td>582</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>47.000</td>\n",
       "      <td>30.300</td>\n",
       "      <td>6.000</td>\n",
       "      <td>57446.809</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>55.034</td>\n",
       "      <td>82.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>18.000</td>\n",
       "      <td>9.500</td>\n",
       "      <td>3.500</td>\n",
       "      <td>61111.111</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>54.937</td>\n",
       "      <td>83.106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rooms_Number  Object_Type_ID  Settlement_ID  District_Id  Street_Id  \\\n",
       "0              1               2              1            8       1143   \n",
       "1              1               3              1            1        964   \n",
       "2              1               2              1            1        349   \n",
       "3              2               2              1            9        980   \n",
       "4              3               3              1            7        495   \n",
       "5              3               2              1            9        980   \n",
       "6              1               3              1            9       1835   \n",
       "7              3               3              1            9       1835   \n",
       "8              2               2              1            7        348   \n",
       "9              2               2              1            7        348   \n",
       "10             1               3              1            8       1143   \n",
       "11             1               3              1            8       1143   \n",
       "12             1               3              1            8       1143   \n",
       "13             1               3              1            8       1143   \n",
       "14             1               3              1            8       1143   \n",
       "15             1               3              1            8       1143   \n",
       "16             1               3              1            8       1143   \n",
       "17             1               2              1            7        348   \n",
       "18             2               2              1            7        348   \n",
       "19             1               3              1            8       1143   \n",
       "\n",
       "    Metro_ID  Metro_m  Flats_Plan_ID  Stor  Storeys  Wall_ID  Space_Total  \\\n",
       "0        291        0             23     4       18        3       22.000   \n",
       "1        284      683              3     8       14        2       42.700   \n",
       "2        284      366              0     6        9        1       29.900   \n",
       "3        291        0              3     3        9        1       46.300   \n",
       "4        284     1150              3     2       10        1       66.000   \n",
       "5        291        0              2     4       10        1       59.000   \n",
       "6        291        0              3     9       10        2       47.000   \n",
       "7        291        0              3    10       10        1       64.000   \n",
       "8        284      682              3     5        9        1       46.000   \n",
       "9        284      568              3     4        9        1       46.600   \n",
       "10       291        0              0     4       17        1       21.800   \n",
       "11       291        0              0     2       18        1       21.900   \n",
       "12       291        0              3     6       17        3       25.600   \n",
       "13       291        0              0     7       18        1       29.000   \n",
       "14       291        0              0    12       17        3       36.200   \n",
       "15       291        0              0    12       17        3       23.400   \n",
       "16       291        0              0     9       18        3       23.300   \n",
       "17       284      471              3     9        9        2       34.200   \n",
       "18       284      582              3     4        5        1       47.000   \n",
       "19       291        0              0     9       17        3       18.000   \n",
       "\n",
       "    Space_Living  Space_Kitchen   Value_m  Balcon_Num  Sost_ID  Clozet_ID  \\\n",
       "0         12.000          0.000 60909.091           0        6          8   \n",
       "1         22.000         11.800 76112.412           0        2          8   \n",
       "2         17.200          5.600 71906.355           0        7          8   \n",
       "3         28.500          7.100 49676.026           1        0          9   \n",
       "4         42.000          9.000 44696.970           0        2          9   \n",
       "5         38.000         10.000 47457.627           1        2          9   \n",
       "6         31.200          0.000 67659.574           0        5          8   \n",
       "7         39.000         11.000 59375.000           0        6          9   \n",
       "8         27.000          7.000 65217.391           1        2          9   \n",
       "9         28.600          7.200 60085.837           1        2          9   \n",
       "10        12.700          3.200 59633.028           0        6          8   \n",
       "11        12.200          3.300 55936.073           0        2          8   \n",
       "12        13.900          6.800 70703.125           0        6          8   \n",
       "13        13.000          9.000 56896.552           0        2          8   \n",
       "14        18.000         13.000 45580.110           0        9          8   \n",
       "15        15.600          0.000 44230.769           0        9          8   \n",
       "16        14.000          0.000 54506.438           1        6          8   \n",
       "17        18.400          6.300 54093.567           1        7          8   \n",
       "18        30.300          6.000 57446.809           1        2          9   \n",
       "19         9.500          3.500 61111.111           0        6          8   \n",
       "\n",
       "      lat   Long  \n",
       "0  54.937 83.101  \n",
       "1  55.043 82.982  \n",
       "2  55.041 82.980  \n",
       "3  54.890 83.081  \n",
       "4  55.029 82.970  \n",
       "5  54.890 83.077  \n",
       "6  54.888 83.096  \n",
       "7  54.889 83.100  \n",
       "8  55.033 82.976  \n",
       "9  55.034 82.976  \n",
       "10 54.938 83.111  \n",
       "11 54.938 83.111  \n",
       "12 54.938 83.111  \n",
       "13 54.938 83.111  \n",
       "14 54.938 83.105  \n",
       "15 54.937 83.106  \n",
       "16 54.938 83.105  \n",
       "17 55.036 82.969  \n",
       "18 55.034 82.970  \n",
       "19 54.937 83.106  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms_Number</th>\n",
       "      <th>Object_Type_ID</th>\n",
       "      <th>Settlement_ID</th>\n",
       "      <th>District_Id</th>\n",
       "      <th>Street_Id</th>\n",
       "      <th>Metro_ID</th>\n",
       "      <th>Metro_m</th>\n",
       "      <th>Flats_Plan_ID</th>\n",
       "      <th>Stor</th>\n",
       "      <th>Storeys</th>\n",
       "      <th>Wall_ID</th>\n",
       "      <th>Space_Total</th>\n",
       "      <th>Space_Living</th>\n",
       "      <th>Space_Kitchen</th>\n",
       "      <th>Value_m</th>\n",
       "      <th>Balcon_Num</th>\n",
       "      <th>Sost_ID</th>\n",
       "      <th>Clozet_ID</th>\n",
       "      <th>lat</th>\n",
       "      <th>Long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "      <td>34856.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.759</td>\n",
       "      <td>2.968</td>\n",
       "      <td>44.367</td>\n",
       "      <td>7.079</td>\n",
       "      <td>1866.086</td>\n",
       "      <td>272.121</td>\n",
       "      <td>3047.093</td>\n",
       "      <td>4.282</td>\n",
       "      <td>5.312</td>\n",
       "      <td>9.375</td>\n",
       "      <td>2.440</td>\n",
       "      <td>45.638</td>\n",
       "      <td>26.007</td>\n",
       "      <td>7.223</td>\n",
       "      <td>55475.020</td>\n",
       "      <td>0.428</td>\n",
       "      <td>4.235</td>\n",
       "      <td>7.944</td>\n",
       "      <td>54.973</td>\n",
       "      <td>82.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.825</td>\n",
       "      <td>3.853</td>\n",
       "      <td>236.387</td>\n",
       "      <td>7.150</td>\n",
       "      <td>2314.029</td>\n",
       "      <td>65.507</td>\n",
       "      <td>3061.363</td>\n",
       "      <td>6.650</td>\n",
       "      <td>3.916</td>\n",
       "      <td>5.012</td>\n",
       "      <td>8.025</td>\n",
       "      <td>25.280</td>\n",
       "      <td>16.392</td>\n",
       "      <td>4.419</td>\n",
       "      <td>14442.254</td>\n",
       "      <td>0.523</td>\n",
       "      <td>2.677</td>\n",
       "      <td>2.048</td>\n",
       "      <td>1.181</td>\n",
       "      <td>1.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>615.000</td>\n",
       "      <td>283.000</td>\n",
       "      <td>451.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>17.000</td>\n",
       "      <td>5.700</td>\n",
       "      <td>46210.721</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>54.964</td>\n",
       "      <td>82.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>1093.000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>2001.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43.000</td>\n",
       "      <td>25.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>54054.054</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>55.005</td>\n",
       "      <td>82.945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>1608.000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>5530.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>56.500</td>\n",
       "      <td>33.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>63636.364</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>55.047</td>\n",
       "      <td>82.993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000</td>\n",
       "      <td>44.000</td>\n",
       "      <td>1673.000</td>\n",
       "      <td>64.000</td>\n",
       "      <td>12819.000</td>\n",
       "      <td>294.000</td>\n",
       "      <td>15000.000</td>\n",
       "      <td>43.000</td>\n",
       "      <td>27.000</td>\n",
       "      <td>28.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>3217.000</td>\n",
       "      <td>1806.000</td>\n",
       "      <td>101.000</td>\n",
       "      <td>544512.195</td>\n",
       "      <td>4.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>57.193</td>\n",
       "      <td>84.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rooms_Number  Object_Type_ID  Settlement_ID  District_Id  Street_Id  \\\n",
       "count     34856.000       34856.000      34856.000    34856.000  34856.000   \n",
       "mean          1.759           2.968         44.367        7.079   1866.086   \n",
       "std           0.825           3.853        236.387        7.150   2314.029   \n",
       "min           1.000           1.000          1.000        1.000      6.000   \n",
       "25%           1.000           2.000          1.000        4.000    615.000   \n",
       "50%           2.000           3.000          1.000        6.000   1093.000   \n",
       "75%           2.000           3.000          1.000        7.000   1608.000   \n",
       "max           4.000          44.000       1673.000       64.000  12819.000   \n",
       "\n",
       "       Metro_ID   Metro_m  Flats_Plan_ID      Stor   Storeys   Wall_ID  \\\n",
       "count 34856.000 34856.000      34856.000 34856.000 34856.000 34856.000   \n",
       "mean    272.121  3047.093          4.282     5.312     9.375     2.440   \n",
       "std      65.507  3061.363          6.650     3.916     5.012     8.025   \n",
       "min       0.000     0.000          0.000     1.000     0.000     0.000   \n",
       "25%     283.000   451.000          1.000     2.000     5.000     1.000   \n",
       "50%     290.000  2001.000          3.000     4.000     9.000     2.000   \n",
       "75%     290.000  5530.000          3.000     7.000    10.000     2.000   \n",
       "max     294.000 15000.000         43.000    27.000    28.000    95.000   \n",
       "\n",
       "       Space_Total  Space_Living  Space_Kitchen    Value_m  Balcon_Num  \\\n",
       "count    34856.000     34856.000      34856.000  34856.000   34856.000   \n",
       "mean        45.638        26.007          7.223  55475.020       0.428   \n",
       "std         25.280        16.392          4.419  14442.254       0.523   \n",
       "min          0.000         0.000          0.000      0.000       0.000   \n",
       "25%         32.000        17.000          5.700  46210.721       0.000   \n",
       "50%         43.000        25.000          7.000  54054.054       0.000   \n",
       "75%         56.500        33.000          9.000  63636.364       1.000   \n",
       "max       3217.000      1806.000        101.000 544512.195       4.000   \n",
       "\n",
       "        Sost_ID  Clozet_ID       lat      Long  \n",
       "count 34856.000  34856.000 34856.000 34856.000  \n",
       "mean      4.235      7.944    54.973    82.911  \n",
       "std       2.677      2.048     1.181     1.795  \n",
       "min       0.000      0.000     0.000     0.000  \n",
       "25%       2.000      8.000    54.964    82.899  \n",
       "50%       4.000      8.000    55.005    82.945  \n",
       "75%       6.000      9.000    55.047    82.993  \n",
       "max      11.000     11.000    57.193    84.422  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# смотрим еще раз статистики количественных переменных\n",
    "\n",
    "# увеличиваем количество выводимых столбцов\n",
    "pd.set_option('display.max_columns', 36)\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Видим что есть нулевые значения площадей - Space_Total,Space_Living\n",
    "# нулевые значения Space_Total - то пропуски, которые тоже нужно ка кто импутировать.\n",
    "\n",
    "# нулевые начения переменной Space_Living НЕ записываем как пропуски\n",
    "# так как это может означать нежилые помещения"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# взглянем на значения Space_Total менее 10\n",
    "data[data['Space_Total'] < 10]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# значения переменной Space_Total меньше 1 записываем как пропуски\n",
    "data['Space_Total'] = np.where(data['Space_Total'] < 1, None , data['Space_Total'])\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# взглянем еще раз на значения Space_Total менее 10\n",
    "data[data['Space_Total'] < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Далее, возможно большие выбросы в большую сторону по переменным Space_Total,Space_Living,Space_Kitchen.\n",
    "### указываюь на редкое элитное жилье, типа особняков с огромными площадями,\n",
    "### или на большие промышленные помещения.\n",
    "### Посмотрим на данные записи :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4663    3217.000\n",
       "6405     655.000\n",
       "3828     275.200\n",
       "21031    260.000\n",
       "29596    260.000\n",
       "23416    254.000\n",
       "23472    232.700\n",
       "15825    218.000\n",
       "23240    218.000\n",
       "18396    205.000\n",
       "Name: Space_Total, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выведем 10 наибольших значений Space_Total\n",
    "data['Space_Total'].nlargest(10)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Есть всег одно огромне помещение в наборе данных\n",
    "\n",
    "# Посмотрим на все помещения больше определенного значения\n",
    "data[data['Space_Total'] > 200 ]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Видим, что есть вего две непонятные записи с выбросами по Space_Total в наборе данных -\n",
    "# недвижимость площадью 3217 и жилой площадью - 7 и одной комнатой\n",
    "# и площадью 655 и жилой площадью - 46 и одной комнатой\n",
    "# Похоже, что это ошибка ввода данных. Заменим  на пропуск все что выше порога  600 .\n",
    "# значения переменной Space_Total меньше 1 записываем как пропуски\n",
    "data['Space_Total'] = np.where(data['Space_Total'] > 600, 0, data['Space_Total'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# выведем 10 наибольших значений Space_Living\n",
    "data['Space_Living'].nlargest(10)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Посмотрим теперь на все помещения с жилым помещением больше определенного значения\n",
    "data[data['Space_Living'] > 200 ]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Есть всего одно помещение с огромным значением Space_Living в наборе данных\n",
    "# и в нем значение Space_Living больше чем общая площадь,\n",
    "# что означает ошибку в наборе данных\n",
    "\n",
    "# Посмотрим теперь на все помещения с жилым помещением больше общей площади\n",
    "data[data['Space_Living']/data['Space_Total']  > 1 ]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Так как есть такие ошибочные записи,\n",
    "# то в них значения переменной Space_Living записываем как пропуски\n",
    "data['Space_Living'] = np.where(data['Space_Living']/data['Space_Total']  > 1 , 0, data['Space_Living'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Посмотрим теперь на все помещения с жилым помещением больше общей площади\n",
    "data[data['Space_Kitchen']/data['Space_Total']  > 1 ]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Так как есть такие ошибочные записи,\n",
    "# то в них значения переменной Space_Kitchen записываем как пропуски\n",
    "data['Space_Kitchen'] = np.where(data['Space_Kitchen']/data['Space_Total']  > 1 , 0, data['Space_Kitchen'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7) Конструирование признаков, котор.можно выполнить до разбиения/перекр.проверки."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Так же, есть помещения относятся помещения с жилой площадью равной нулю.\n",
    "Возможно они являются промышленными?\n",
    "И возможно в дальнейшем стоит попробовать либо выделить их в отдельную категроию\n",
    "\n",
    "Но пока то мы сконструируем дополнительные признаки, указывающие на:\n",
    "1) возможное нежилое помещение, т.е. с нулевой жилой площадью,\n",
    "2) отсутствием кухни(студии или промыщленные помещения?, или незаполненные данные?)),\n",
    "3) соотношением  жилой площади к общей ( это нужно будет сделать ТОЛЬКО ПОСЛЕ ИМПУТАЦИИ ПРОПУСКОВ)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms_Number</th>\n",
       "      <th>Object_Type_ID</th>\n",
       "      <th>Settlement_ID</th>\n",
       "      <th>District_Id</th>\n",
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       "      <th>Space_Living</th>\n",
       "      <th>Space_Kitchen</th>\n",
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       "      <th>Balcon_Num</th>\n",
       "      <th>Sost_ID</th>\n",
       "      <th>Clozet_ID</th>\n",
       "      <th>lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Nonresidential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>645</td>\n",
       "      <td>284</td>\n",
       "      <td>2160</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>37.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>52702.703</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>55.052</td>\n",
       "      <td>82.993</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1361</td>\n",
       "      <td>33</td>\n",
       "      <td>6014</td>\n",
       "      <td>291</td>\n",
       "      <td>2061</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>43.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.600</td>\n",
       "      <td>58179.724</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>54.945</td>\n",
       "      <td>83.194</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>964</td>\n",
       "      <td>284</td>\n",
       "      <td>1250</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>55.039</td>\n",
       "      <td>82.986</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>125</td>\n",
       "      <td>291</td>\n",
       "      <td>5250</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>44.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>35454.545</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54.751</td>\n",
       "      <td>83.090</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1361</td>\n",
       "      <td>33</td>\n",
       "      <td>8330</td>\n",
       "      <td>291</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>67391.304</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>54.938</td>\n",
       "      <td>83.196</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>34798</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>927</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>34799</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>927</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>41.900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>71599.045</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>55.136</td>\n",
       "      <td>82.894</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34808</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1375</td>\n",
       "      <td>289</td>\n",
       "      <td>872</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>44.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>68181.818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.025</td>\n",
       "      <td>82.909</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>34829</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>54.761</td>\n",
       "      <td>83.102</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34840</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1410</td>\n",
       "      <td>288</td>\n",
       "      <td>634</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>56.300</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>67495.560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.041</td>\n",
       "      <td>82.898</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1691 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rooms_Number  Object_Type_ID  Settlement_ID  District_Id  Street_Id  \\\n",
       "28                1               3              1            1        645   \n",
       "37                1               3           1361           33       6014   \n",
       "38                2               1              1            7        964   \n",
       "44                2               2              2           15        125   \n",
       "119               1               3           1361           33       8330   \n",
       "...             ...             ...            ...          ...        ...   \n",
       "34798             2               3              1            3        927   \n",
       "34799             2               3              1            3        927   \n",
       "34808             2               2              1            2       1375   \n",
       "34829             2               1              2           15         62   \n",
       "34840             1               3              1            2       1410   \n",
       "\n",
       "       Metro_ID  Metro_m  Flats_Plan_ID  Stor  Storeys  Wall_ID  Space_Total  \\\n",
       "28          284     2160             23     6        9        2       37.000   \n",
       "37          291     2061              0     8        9        2       43.400   \n",
       "38          284     1250              3     2        5        1        0.000   \n",
       "44          291     5250              3     5        5        1       44.000   \n",
       "119         291       18             23     7        9        2       23.000   \n",
       "...         ...      ...            ...   ...      ...      ...          ...   \n",
       "34798         0        0             23     6       10        2       41.900   \n",
       "34799         0        0             23     6       10        2       41.900   \n",
       "34808       289      872              0     7        9        2       44.000   \n",
       "34829         0        0              0     2        4        2        0.000   \n",
       "34840       288      634              0     8       16        2       56.300   \n",
       "\n",
       "       Space_Living  Space_Kitchen   Value_m  Balcon_Num  Sost_ID  Clozet_ID  \\\n",
       "28            0.000          0.000 52702.703           0        2          8   \n",
       "37            0.000          9.600 58179.724           0        2          8   \n",
       "38            0.000          0.000     0.000           1        2          8   \n",
       "44            0.000          0.000 35454.545           1        0          0   \n",
       "119           0.000          0.000 67391.304           0        9          8   \n",
       "...             ...            ...       ...         ...      ...        ...   \n",
       "34798         0.000          0.000 71599.045           1        9          8   \n",
       "34799         0.000          0.000 71599.045           1        9          8   \n",
       "34808         0.000          0.000 68181.818           0        0          0   \n",
       "34829         0.000          0.000     0.000           1        2          0   \n",
       "34840         0.000          0.000 67495.560           0        0          0   \n",
       "\n",
       "         lat   Long  Nonresidential  \n",
       "28    55.052 82.993               1  \n",
       "37    54.945 83.194               1  \n",
       "38    55.039 82.986               1  \n",
       "44    54.751 83.090               1  \n",
       "119   54.938 83.196               1  \n",
       "...      ...    ...             ...  \n",
       "34798 55.136 82.894               1  \n",
       "34799 55.136 82.894               1  \n",
       "34808 55.025 82.909               1  \n",
       "34829 54.761 83.102               1  \n",
       "34840 55.041 82.898               1  \n",
       "\n",
       "[1691 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # создаем индикатор возможного нежилого помещения\n",
    "data['Nonresidential'] = np.where(\n",
    "    data['Space_Living'] == 0, 1, 0)\n",
    "\n",
    "data[data['Space_Living'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <th>lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Nonresidential</th>\n",
       "      <th>WithoutKitchen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>22.000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>60909.091</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>54.937</td>\n",
       "      <td>83.101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1835</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>47.000</td>\n",
       "      <td>31.200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>67659.574</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>54.888</td>\n",
       "      <td>83.096</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>23.400</td>\n",
       "      <td>15.600</td>\n",
       "      <td>0.000</td>\n",
       "      <td>44230.769</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>54.937</td>\n",
       "      <td>83.106</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>23.300</td>\n",
       "      <td>14.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>54506.438</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>54.938</td>\n",
       "      <td>83.105</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>964</td>\n",
       "      <td>284</td>\n",
       "      <td>1100</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>36.000</td>\n",
       "      <td>18.200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>76111.111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.045</td>\n",
       "      <td>82.983</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34798</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>927</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>41.900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>71599.045</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>55.136</td>\n",
       "      <td>82.894</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34799</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>927</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>41.900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>71599.045</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>55.136</td>\n",
       "      <td>82.894</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34808</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1375</td>\n",
       "      <td>289</td>\n",
       "      <td>872</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>44.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>68181.818</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.025</td>\n",
       "      <td>82.909</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34829</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>54.761</td>\n",
       "      <td>83.102</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34840</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1410</td>\n",
       "      <td>288</td>\n",
       "      <td>634</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>56.300</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>67495.560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.041</td>\n",
       "      <td>82.898</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4119 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rooms_Number  Object_Type_ID  Settlement_ID  District_Id  Street_Id  \\\n",
       "0                 1               2              1            8       1143   \n",
       "6                 1               3              1            9       1835   \n",
       "15                1               3              1            8       1143   \n",
       "16                1               3              1            8       1143   \n",
       "25                1               3              1            1        964   \n",
       "...             ...             ...            ...          ...        ...   \n",
       "34798             2               3              1            3        927   \n",
       "34799             2               3              1            3        927   \n",
       "34808             2               2              1            2       1375   \n",
       "34829             2               1              2           15         62   \n",
       "34840             1               3              1            2       1410   \n",
       "\n",
       "       Metro_ID  Metro_m  Flats_Plan_ID  Stor  Storeys  Wall_ID  Space_Total  \\\n",
       "0           291        0             23     4       18        3       22.000   \n",
       "6           291        0              3     9       10        2       47.000   \n",
       "15          291        0              0    12       17        3       23.400   \n",
       "16          291        0              0     9       18        3       23.300   \n",
       "25          284     1100              0    16       16        2       36.000   \n",
       "...         ...      ...            ...   ...      ...      ...          ...   \n",
       "34798         0        0             23     6       10        2       41.900   \n",
       "34799         0        0             23     6       10        2       41.900   \n",
       "34808       289      872              0     7        9        2       44.000   \n",
       "34829         0        0              0     2        4        2        0.000   \n",
       "34840       288      634              0     8       16        2       56.300   \n",
       "\n",
       "       Space_Living  Space_Kitchen   Value_m  Balcon_Num  Sost_ID  Clozet_ID  \\\n",
       "0            12.000          0.000 60909.091           0        6          8   \n",
       "6            31.200          0.000 67659.574           0        5          8   \n",
       "15           15.600          0.000 44230.769           0        9          8   \n",
       "16           14.000          0.000 54506.438           1        6          8   \n",
       "25           18.200          0.000 76111.111           0        0          0   \n",
       "...             ...            ...       ...         ...      ...        ...   \n",
       "34798         0.000          0.000 71599.045           1        9          8   \n",
       "34799         0.000          0.000 71599.045           1        9          8   \n",
       "34808         0.000          0.000 68181.818           0        0          0   \n",
       "34829         0.000          0.000     0.000           1        2          0   \n",
       "34840         0.000          0.000 67495.560           0        0          0   \n",
       "\n",
       "         lat   Long  Nonresidential  WithoutKitchen  \n",
       "0     54.937 83.101               0               1  \n",
       "6     54.888 83.096               0               1  \n",
       "15    54.937 83.106               0               1  \n",
       "16    54.938 83.105               0               1  \n",
       "25    55.045 82.983               0               1  \n",
       "...      ...    ...             ...             ...  \n",
       "34798 55.136 82.894               1               1  \n",
       "34799 55.136 82.894               1               1  \n",
       "34808 55.025 82.909               1               1  \n",
       "34829 54.761 83.102               1               1  \n",
       "34840 55.041 82.898               1               1  \n",
       "\n",
       "[4119 rows x 22 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# создаем индикатор помещения без кухни (студии или промыщленные помещения?, или незаполненные данные?)\n",
    "data['WithoutKitchen'] = np.where(\n",
    "    data['Space_Kitchen'] == 0, 1, 0)\n",
    "\n",
    "data[data['Space_Kitchen'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Оценим графически распределение переменных :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# функция, которая строит гистограммы\n",
    "# распределения и графики квантиль-квантиль\n",
    "def plot_hist(df):\n",
    "    # создаем копию датафрейма\n",
    "    df_ = df.copy()\n",
    "    # отбираем столбцы, у которых больше 20 уникальных значений\n",
    "    df_ = df_.loc[:, df.apply(pd.Series.nunique) > 20]\n",
    "    # из этих столбцов отбираем только количественные\n",
    "    num_cols = df_.select_dtypes(include=['number']).columns\n",
    "    for col in num_cols:\n",
    "        df_[col].fillna(df_[col].median(), inplace=True)\n",
    "        # строим гистограмму\n",
    "        sns.distplot(df_[col], fit=norm)\n",
    "        fig = plt.figure();\n",
    "        # строим график квантиль-квантиль\n",
    "        stats.probplot(df_[col], plot=plt)\n",
    "        fig = plt.figure();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\Cv1\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "D:\\Anaconda3\\envs\\Cv1\\lib\\site-packages\\ipykernel_launcher.py:17: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "D:\\Anaconda3\\envs\\Cv1\\lib\\site-packages\\ipykernel_launcher.py:14: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  \n",
      "D:\\Anaconda3\\envs\\Cv1\\lib\\site-packages\\ipykernel_launcher.py:17: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "D:\\Anaconda3\\envs\\Cv1\\lib\\site-packages\\ipykernel_launcher.py:14: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  \n",
      "D:\\Anaconda3\\envs\\Cv1\\lib\\site-packages\\ipykernel_launcher.py:17: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "D:\\Anaconda3\\envs\\Cv1\\lib\\site-packages\\ipykernel_launcher.py:14: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  \n",
      "D:\\Anaconda3\\envs\\Cv1\\lib\\site-packages\\ipykernel_launcher.py:17: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# применяем функцию plot_hist()\n",
    "plot_hist(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  в зависимой переменной Value_m какие то нулевые значение(что ввроде бы не может быть) \n",
    "# и так же подозрительно резко  большие(до 544512.195 !!! )\n",
    "# ХОТЕЛОСЬ БЫ ПРОЯСНИТЬ ЧТО ЭТО !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Очистка по зависимой переменной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34856, 22)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.query(\"Value_m != 0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34818, 22)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9547    544512.195\n",
       "1175    190476.190\n",
       "27961   169902.913\n",
       "8816    160621.762\n",
       "10473   153409.091\n",
       "           ...    \n",
       "10368   107594.937\n",
       "32196   107352.941\n",
       "6742    107142.857\n",
       "9093    107023.411\n",
       "10595   106896.552\n",
       "Name: Value_m, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выведем 10 наибольших значений Value_m\n",
    "data['Value_m'].nlargest(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.query(\"Value_m < 200000\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34817, 22)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Для борьбы с выбросами, возможно хорошо было бы применить к перееменным Metro_m, Space_Total,Space_Living,Space_Kitchen,\n",
    "# преобразование максимизирующее нормальность  или виндризацию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# функция, вычисляющая скос \n",
    "# после преобразований\n",
    "def diagnostics_skewness(df, strategy):\n",
    "    # создаем списки\n",
    "    col_list = df.select_dtypes(include=['number']).columns\n",
    "    skew_initial_list = []\n",
    "    skew_pos_reciprocal_list = []\n",
    "    skew_neg_reciprocal_list = []\n",
    "    skew_log_list = []\n",
    "    skew_corr_log_001_list = []\n",
    "    skew_corr_log_01_list = []\n",
    "    skew_corr_log_1_list = []\n",
    "    skew_corr_log_5_list = []\n",
    "    skew_cbrt_list = []\n",
    "    skew_sqrt_list = []\n",
    "    \n",
    "    # создаем копию датафрейма\n",
    "    df_ = df.copy()\n",
    "    # запускаем цикл, который вычисляет скос по каждой \n",
    "    # преобразованной переменной\n",
    "    for col in col_list:\n",
    "        # импутируем пропуски в зависимости от заданной стратегии\n",
    "        if strategy == 'median':\n",
    "            df_[col] = df_[col].fillna(df_[col].median())\n",
    "        if strategy == 'mean':\n",
    "            df_[col] = df_[col].fillna(df_[col].mean())\n",
    "        if strategy == 'zero':\n",
    "            df_[col] = df_[col].fillna(0)\n",
    "            \n",
    "        skew_initial = df_[col].skew()\n",
    "        skew_pos_reciprocal = (1 / (df_[col].clip(0.01))).skew()\n",
    "        skew_neg_reciprocal = (-1 / (df_[col].clip(0.01))).skew()\n",
    "        skew_log = np.log(df[col].clip(0.01)).skew()\n",
    "        skew_corr_log_001 = np.log((df_[col].clip(0.01) / df_[col].mean()) + 0.001).skew()\n",
    "        skew_corr_log_01 = np.log((df_[col].clip(0.01) / df_[col].mean()) + 0.01).skew()\n",
    "        skew_corr_log_1 = np.log((df_[col].clip(0.01) / df_[col].mean()) + 0.1).skew()\n",
    "        skew_corr_log_5 = np.log((df_[col].clip(0.01) / df_[col].mean()) + 0.5).skew()\n",
    "        skew_cbrt = np.cbrt(df_[col].abs()).skew()\n",
    "        skew_sqrt = np.sqrt(df_[col].abs()).skew()\n",
    "        skew_initial_list.append(skew_initial)\n",
    "        skew_pos_reciprocal_list.append(skew_pos_reciprocal)\n",
    "        skew_neg_reciprocal_list.append(skew_neg_reciprocal)\n",
    "        skew_log_list.append(skew_log)\n",
    "        skew_corr_log_001_list.append(skew_corr_log_001)\n",
    "        skew_corr_log_01_list.append(skew_corr_log_01)\n",
    "        skew_corr_log_1_list.append(skew_corr_log_1)\n",
    "        skew_corr_log_5_list.append(skew_corr_log_5)\n",
    "        skew_cbrt_list.append(skew_cbrt)\n",
    "        skew_sqrt_list.append(skew_sqrt)   \n",
    "\n",
    "    result = pd.DataFrame({'Переменная': col_list,\n",
    "                           'Skew_init': skew_initial_list,\n",
    "                           'Skew_pos_recip': skew_pos_reciprocal_list,\n",
    "                           'Skew_neg_recip': skew_neg_reciprocal_list,\n",
    "                           'Skew_log': skew_log_list,\n",
    "                           'Skew_adj_log (k=0.001)': skew_corr_log_001_list,\n",
    "                           'Skew_adj_log (k=0.01)': skew_corr_log_01_list,\n",
    "                           'Skew_adj_log (k=0.1)': skew_corr_log_1_list,\n",
    "                           'Skew_adj_log (k=0.5)': skew_corr_log_5_list,\n",
    "                           'Skew_cbrt': skew_cbrt_list,\n",
    "                           'Skew_sqrt': skew_sqrt_list})\n",
    "    result = np.round(result.sort_values(by='Skew_init', ascending=False), 3)\n",
    "    cm = sns.light_palette('magenta', as_cmap=True)\n",
    "    return(result.style.background_gradient(cmap=cm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "            background-color:  #ffe2ff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ff04ff;\n",
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       "            background-color:  #ff0aff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff0aff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff0aff;\n",
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       "            background-color:  #ff0bff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff0cff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff0cff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff17ff;\n",
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       "            background-color:  #ff7aff;\n",
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       "            background-color:  #ffdbff;\n",
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       "            background-color:  #ff0bff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff19ff;\n",
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       "            background-color:  #ff18ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff18ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff15ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col8 {\n",
       "            background-color:  #ff10ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff16ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff1cff;\n",
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       "            background-color:  #ff7aff;\n",
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       "            background-color:  #ffb3ff;\n",
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       "            background-color:  #ff15ff;\n",
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       "            background-color:  #ff14ff;\n",
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       "            background-color:  #ff14ff;\n",
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       "            background-color:  #ff12ff;\n",
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       "            background-color:  #ff10ff;\n",
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       "            background-color:  #ff11ff;\n",
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       "            background-color:  #ff1aff;\n",
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       "            background-color:  #ff7bff;\n",
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       "            background-color:  #ff0eff;\n",
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       "            background-color:  #ff1fff;\n",
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       "            background-color:  #ff1fff;\n",
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       "            background-color:  #ff1eff;\n",
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       "            background-color:  #ff1dff;\n",
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       "            background-color:  #ff1aff;\n",
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       "            background-color:  #ff1eff;\n",
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       "            background-color:  #ff0cff;\n",
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       "            background-color:  #ff16ff;\n",
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       "            background-color:  #ff16ff;\n",
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       "            background-color:  #ff16ff;\n",
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       "            background-color:  #ff16ff;\n",
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       "            background-color:  #ff15ff;\n",
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       "            background-color:  #ff15ff;\n",
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       "            background-color:  #ff1fff;\n",
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       "            background-color:  #ff7dff;\n",
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       "            background-color:  #ffe5ff;\n",
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       "            background-color:  #ff17ff;\n",
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       "            background-color:  #ff17ff;\n",
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       "            background-color:  #ff16ff;\n",
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       "            background-color:  #ff16ff;\n",
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       "            background-color:  #ff1aff;\n",
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       "            background-color:  #ff18ff;\n",
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       "            background-color:  #ff17ff;\n",
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       "            background-color:  #ff18ff;\n",
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       "            background-color:  #ff20ff;\n",
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       "            background-color:  #ff13ff;\n",
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       "            background-color:  #ff13ff;\n",
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       "            background-color:  #ff14ff;\n",
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       "            background-color:  #ff14ff;\n",
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       "            background-color:  #ff15ff;\n",
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       "            background-color:  #ff15ff;\n",
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       "            background-color:  #ff1fff;\n",
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       "            background-color:  #ffd8ff;\n",
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       "            background-color:  #ff17ff;\n",
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       "            background-color:  #ff17ff;\n",
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       "            background-color:  #ff17ff;\n",
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       "            background-color:  #ff17ff;\n",
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       "            background-color:  #ff17ff;\n",
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       "            background-color:  #ff17ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff20ff;\n",
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       "            background-color:  #ff7eff;\n",
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       "            background-color:  #ffddff;\n",
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       "            background-color:  #ff08ff;\n",
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       "            background-color:  #ff13ff;\n",
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       "            background-color:  #ff13ff;\n",
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       "            background-color:  #ff13ff;\n",
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       "            background-color:  #ff14ff;\n",
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       "            background-color:  #ff15ff;\n",
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       "            background-color:  #ff15ff;\n",
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       "            background-color:  #ff1fff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff7fff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ffd5ff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ff10ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff21ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col5 {\n",
       "            background-color:  #ff21ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff1fff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff1aff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col8 {\n",
       "            background-color:  #ff17ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff1dff;\n",
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       "            background-color:  #ff23ff;\n",
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       "            background-color:  #ff87ff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ffd5ff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ff10ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff26ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff26ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff26ff;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #ff26ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col8 {\n",
       "            background-color:  #ff26ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col9 {\n",
       "            background-color:  #ff27ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col10 {\n",
       "            background-color:  #ff31ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col1 {\n",
       "            background-color:  #ff88ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col2 {\n",
       "            background-color:  #ffd5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col3 {\n",
       "            background-color:  #ff10ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col4 {\n",
       "            background-color:  #ff26ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col5 {\n",
       "            background-color:  #ff26ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col6 {\n",
       "            background-color:  #ff26ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col7 {\n",
       "            background-color:  #ff27ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col8 {\n",
       "            background-color:  #ff28ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col9 {\n",
       "            background-color:  #ff28ff;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col10 {\n",
       "            background-color:  #ff31ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col1 {\n",
       "            background-color:  #ffe4ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col2 {\n",
       "            background-color:  #ff7bff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col3 {\n",
       "            background-color:  #ff6aff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col4 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col5 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col6 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col7 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col8 {\n",
       "            background-color:  #ffe4ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col9 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col10 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ff7bff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ff6aff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col4 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col5 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col6 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col7 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col8 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col9 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }    #T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col10 {\n",
       "            background-color:  #ffe5ff;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Переменная</th>        <th class=\"col_heading level0 col1\" >Skew_init</th>        <th class=\"col_heading level0 col2\" >Skew_pos_recip</th>        <th class=\"col_heading level0 col3\" >Skew_neg_recip</th>        <th class=\"col_heading level0 col4\" >Skew_log</th>        <th class=\"col_heading level0 col5\" >Skew_adj_log (k=0.001)</th>        <th class=\"col_heading level0 col6\" >Skew_adj_log (k=0.01)</th>        <th class=\"col_heading level0 col7\" >Skew_adj_log (k=0.1)</th>        <th class=\"col_heading level0 col8\" >Skew_adj_log (k=0.5)</th>        <th class=\"col_heading level0 col9\" >Skew_cbrt</th>        <th class=\"col_heading level0 col10\" >Skew_sqrt</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row0\" class=\"row_heading level0 row0\" >11</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col0\" class=\"data row0 col0\" >Space_Total</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col1\" class=\"data row0 col1\" >57.957000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col2\" class=\"data row0 col2\" >1.139000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col3\" class=\"data row0 col3\" >-1.139000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col4\" class=\"data row0 col4\" >0.116000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col5\" class=\"data row0 col5\" >0.117000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col6\" class=\"data row0 col6\" >0.130000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col7\" class=\"data row0 col7\" >0.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col8\" class=\"data row0 col8\" >0.602000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col9\" class=\"data row0 col9\" >0.960000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row0_col10\" class=\"data row0 col10\" >2.372000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row1\" class=\"row_heading level0 row1\" >12</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col0\" class=\"data row1 col0\" >Space_Living</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col1\" class=\"data row1 col1\" >37.336000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col2\" class=\"data row1 col2\" >4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col3\" class=\"data row1 col3\" >-4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col4\" class=\"data row1 col4\" >-3.831000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col5\" class=\"data row1 col5\" >-3.672000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col6\" class=\"data row1 col6\" >-3.189000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col7\" class=\"data row1 col7\" >-1.923000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col8\" class=\"data row1 col8\" >-0.647000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col9\" class=\"data row1 col9\" >-2.149000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row1_col10\" class=\"data row1 col10\" >-0.602000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row2\" class=\"row_heading level0 row2\" >10</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col0\" class=\"data row2 col0\" >Wall_ID</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col1\" class=\"data row2 col1\" >10.145000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col2\" class=\"data row2 col2\" >7.746000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col3\" class=\"data row2 col3\" >-7.746000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col4\" class=\"data row2 col4\" >-2.090000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col5\" class=\"data row2 col5\" >-1.834000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col6\" class=\"data row2 col6\" >-0.523000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col7\" class=\"data row2 col7\" >2.298000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col8\" class=\"data row2 col8\" >4.845000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col9\" class=\"data row2 col9\" >5.081000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row2_col10\" class=\"data row2 col10\" >7.920000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row3\" class=\"row_heading level0 row3\" >1</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col0\" class=\"data row3 col0\" >Object_Type_ID</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col1\" class=\"data row3 col1\" >8.069000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col2\" class=\"data row3 col2\" >1.031000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col3\" class=\"data row3 col3\" >-1.031000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col4\" class=\"data row3 col4\" >0.839000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col5\" class=\"data row3 col5\" >0.843000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col6\" class=\"data row3 col6\" >0.878000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col7\" class=\"data row3 col7\" >1.213000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col8\" class=\"data row3 col8\" >2.348000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col9\" class=\"data row3 col9\" >3.040000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row3_col10\" class=\"data row3 col10\" >4.462000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row4\" class=\"row_heading level0 row4\" >2</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col0\" class=\"data row4 col0\" >Settlement_ID</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col1\" class=\"data row4 col1\" >5.498000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col2\" class=\"data row4 col2\" >-3.553000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col3\" class=\"data row4 col3\" >3.553000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col4\" class=\"data row4 col4\" >4.641000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col5\" class=\"data row4 col5\" >4.647000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col6\" class=\"data row4 col6\" >4.691000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col7\" class=\"data row4 col7\" >4.845000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col8\" class=\"data row4 col8\" >5.016000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col9\" class=\"data row4 col9\" >5.081000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row4_col10\" class=\"data row4 col10\" >5.246000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row5\" class=\"row_heading level0 row5\" >20</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col0\" class=\"data row5 col0\" >Nonresidential</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col1\" class=\"data row5 col1\" >4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col2\" class=\"data row5 col2\" >-4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col3\" class=\"data row5 col3\" >4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col4\" class=\"data row5 col4\" >4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col5\" class=\"data row5 col5\" >4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col6\" class=\"data row5 col6\" >4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col7\" class=\"data row5 col7\" >4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col8\" class=\"data row5 col8\" >4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col9\" class=\"data row5 col9\" >4.246000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row5_col10\" class=\"data row5 col10\" >4.246000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row6\" class=\"row_heading level0 row6\" >3</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col0\" class=\"data row6 col0\" >District_Id</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col1\" class=\"data row6 col1\" >3.759000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col2\" class=\"data row6 col2\" >2.236000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col3\" class=\"data row6 col3\" >-2.236000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col4\" class=\"data row6 col4\" >-0.175000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col5\" class=\"data row6 col5\" >-0.169000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col6\" class=\"data row6 col6\" >-0.119000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col7\" class=\"data row6 col7\" >0.281000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col8\" class=\"data row6 col8\" >1.197000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col9\" class=\"data row6 col9\" >1.182000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row6_col10\" class=\"data row6 col10\" >1.900000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row7\" class=\"row_heading level0 row7\" >21</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col0\" class=\"data row7 col0\" >WithoutKitchen</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col1\" class=\"data row7 col1\" >2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col2\" class=\"data row7 col2\" >-2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col3\" class=\"data row7 col3\" >2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col4\" class=\"data row7 col4\" >2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col5\" class=\"data row7 col5\" >2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col6\" class=\"data row7 col6\" >2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col7\" class=\"data row7 col7\" >2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col8\" class=\"data row7 col8\" >2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col9\" class=\"data row7 col9\" >2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row7_col10\" class=\"data row7 col10\" >2.380000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row8\" class=\"row_heading level0 row8\" >7</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col0\" class=\"data row8 col0\" >Flats_Plan_ID</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col1\" class=\"data row8 col1\" >2.370000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col2\" class=\"data row8 col2\" >1.290000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col3\" class=\"data row8 col3\" >-1.290000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col4\" class=\"data row8 col4\" >-0.983000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col5\" class=\"data row8 col5\" >-0.945000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col6\" class=\"data row8 col6\" >-0.735000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col7\" class=\"data row8 col7\" >0.079000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col8\" class=\"data row8 col8\" >1.206000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col9\" class=\"data row8 col9\" >0.091000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row8_col10\" class=\"data row8 col10\" >1.146000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row9\" class=\"row_heading level0 row9\" >4</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col0\" class=\"data row9 col0\" >Street_Id</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col1\" class=\"data row9 col1\" >2.208000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col2\" class=\"data row9 col2\" >20.177000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col3\" class=\"data row9 col3\" >-20.177000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col4\" class=\"data row9 col4\" >0.027000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col5\" class=\"data row9 col5\" >0.064000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col6\" class=\"data row9 col6\" >0.257000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col7\" class=\"data row9 col7\" >0.811000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col8\" class=\"data row9 col8\" >1.378000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col9\" class=\"data row9 col9\" >1.239000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row9_col10\" class=\"data row9 col10\" >1.582000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row10\" class=\"row_heading level0 row10\" >13</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col0\" class=\"data row10 col0\" >Space_Kitchen</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col1\" class=\"data row10 col1\" >1.874000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col2\" class=\"data row10 col2\" >2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col3\" class=\"data row10 col3\" >-2.380000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col4\" class=\"data row10 col4\" >-2.251000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col5\" class=\"data row10 col5\" >-2.228000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col6\" class=\"data row10 col6\" >-2.119000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col7\" class=\"data row10 col7\" >-1.693000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col8\" class=\"data row10 col8\" >-0.910000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col9\" class=\"data row10 col9\" >-1.752000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row10_col10\" class=\"data row10 col10\" >-1.056000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row11\" class=\"row_heading level0 row11\" >8</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col0\" class=\"data row11 col0\" >Stor</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col1\" class=\"data row11 col1\" >1.399000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col2\" class=\"data row11 col2\" >1.461000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col3\" class=\"data row11 col3\" >-1.461000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col4\" class=\"data row11 col4\" >-0.236000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col5\" class=\"data row11 col5\" >-0.234000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col6\" class=\"data row11 col6\" >-0.213000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col7\" class=\"data row11 col7\" >-0.044000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col8\" class=\"data row11 col8\" >0.338000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col9\" class=\"data row11 col9\" >0.279000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row11_col10\" class=\"data row11 col10\" >0.548000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row12\" class=\"row_heading level0 row12\" >9</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col0\" class=\"data row12 col0\" >Storeys</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col1\" class=\"data row12 col1\" >1.045000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col2\" class=\"data row12 col2\" >106.021000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col3\" class=\"data row12 col3\" >-106.021000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col4\" class=\"data row12 col4\" >-0.445000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col5\" class=\"data row12 col5\" >-0.403000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col6\" class=\"data row12 col6\" >-0.312000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col7\" class=\"data row12 col7\" >-0.099000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col8\" class=\"data row12 col8\" >0.258000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col9\" class=\"data row12 col9\" >0.190000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row12_col10\" class=\"data row12 col10\" >0.423000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row13\" class=\"row_heading level0 row13\" >6</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col0\" class=\"data row13 col0\" >Metro_m</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col1\" class=\"data row13 col1\" >0.901000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col2\" class=\"data row13 col2\" >1.700000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col3\" class=\"data row13 col3\" >-1.700000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col4\" class=\"data row13 col4\" >-1.522000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col5\" class=\"data row13 col5\" >-1.197000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col6\" class=\"data row13 col6\" >-0.836000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col7\" class=\"data row13 col7\" >-0.260000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col8\" class=\"data row13 col8\" >0.158000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col9\" class=\"data row13 col9\" >-0.422000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row13_col10\" class=\"data row13 col10\" >0.062000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row14\" class=\"row_heading level0 row14\" >0</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col0\" class=\"data row14 col0\" >Rooms_Number</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col1\" class=\"data row14 col1\" >0.734000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col2\" class=\"data row14 col2\" >-0.011000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col3\" class=\"data row14 col3\" >0.011000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col4\" class=\"data row14 col4\" >0.259000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col5\" class=\"data row14 col5\" >0.260000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col6\" class=\"data row14 col6\" >0.263000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col7\" class=\"data row14 col7\" >0.290000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col8\" class=\"data row14 col8\" >0.381000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col9\" class=\"data row14 col9\" >0.390000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row14_col10\" class=\"data row14 col10\" >0.466000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row15\" class=\"row_heading level0 row15\" >14</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col0\" class=\"data row15 col0\" >Value_m</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col1\" class=\"data row15 col1\" >0.679000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col2\" class=\"data row15 col2\" >99.177000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col3\" class=\"data row15 col3\" >-99.177000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col4\" class=\"data row15 col4\" >-0.649000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col5\" class=\"data row15 col5\" >-0.641000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col6\" class=\"data row15 col6\" >-0.587000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col7\" class=\"data row15 col7\" >-0.384000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col8\" class=\"data row15 col8\" >-0.053000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col9\" class=\"data row15 col9\" >-0.102000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row15_col10\" class=\"data row15 col10\" >0.106000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row16\" class=\"row_heading level0 row16\" >15</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col0\" class=\"data row16 col0\" >Balcon_Num</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col1\" class=\"data row16 col1\" >0.626000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col2\" class=\"data row16 col2\" >-0.345000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col3\" class=\"data row16 col3\" >0.345000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col4\" class=\"data row16 col4\" >0.349000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col5\" class=\"data row16 col5\" >0.349000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col6\" class=\"data row16 col6\" >0.350000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col7\" class=\"data row16 col7\" >0.355000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col8\" class=\"data row16 col8\" >0.371000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col9\" class=\"data row16 col9\" >0.359000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row16_col10\" class=\"data row16 col10\" >0.382000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row17\" class=\"row_heading level0 row17\" >16</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col0\" class=\"data row17 col0\" >Sost_ID</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col1\" class=\"data row17 col1\" >0.404000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col2\" class=\"data row17 col2\" >3.823000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col3\" class=\"data row17 col3\" >-3.823000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col4\" class=\"data row17 col4\" >-2.878000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col5\" class=\"data row17 col5\" >-2.783000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col6\" class=\"data row17 col6\" >-2.311000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col7\" class=\"data row17 col7\" >-1.101000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col8\" class=\"data row17 col8\" >-0.266000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col9\" class=\"data row17 col9\" >-1.457000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row17_col10\" class=\"data row17 col10\" >-0.558000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row18\" class=\"row_heading level0 row18\" >17</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col0\" class=\"data row18 col0\" >Clozet_ID</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col1\" class=\"data row18 col1\" >-3.284000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col2\" class=\"data row18 col2\" >3.883000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col3\" class=\"data row18 col3\" >-3.883000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col4\" class=\"data row18 col4\" >-3.853000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col5\" class=\"data row18 col5\" >-3.847000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col6\" class=\"data row18 col6\" >-3.822000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col7\" class=\"data row18 col7\" >-3.734000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col8\" class=\"data row18 col8\" >-3.592000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col9\" class=\"data row18 col9\" >-3.773000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row18_col10\" class=\"data row18 col10\" >-3.672000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row19\" class=\"row_heading level0 row19\" >5</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col0\" class=\"data row19 col0\" >Metro_ID</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col1\" class=\"data row19 col1\" >-3.903000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col2\" class=\"data row19 col2\" >3.922000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col3\" class=\"data row19 col3\" >-3.922000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col4\" class=\"data row19 col4\" >-3.922000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col5\" class=\"data row19 col5\" >-3.922000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col6\" class=\"data row19 col6\" >-3.921000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col7\" class=\"data row19 col7\" >-3.920000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col8\" class=\"data row19 col8\" >-3.915000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col9\" class=\"data row19 col9\" >-3.920000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row19_col10\" class=\"data row19 col10\" >-3.917000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row20\" class=\"row_heading level0 row20\" >19</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col0\" class=\"data row20 col0\" >Long</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col1\" class=\"data row20 col1\" >-45.423000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col2\" class=\"data row20 col2\" >46.618000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col3\" class=\"data row20 col3\" >-46.618000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col4\" class=\"data row20 col4\" >-46.595000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col5\" class=\"data row20 col5\" >-46.579000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col6\" class=\"data row20 col6\" >-46.535000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col7\" class=\"data row20 col7\" >-46.375000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col8\" class=\"data row20 col8\" >-46.066000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col9\" class=\"data row20 col9\" >-46.440000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row20_col10\" class=\"data row20 col10\" >-46.247000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1level0_row21\" class=\"row_heading level0 row21\" >18</th>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col0\" class=\"data row21 col0\" >lat</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col1\" class=\"data row21 col1\" >-46.243000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col2\" class=\"data row21 col2\" >46.618000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col3\" class=\"data row21 col3\" >-46.618000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col4\" class=\"data row21 col4\" >-46.613000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col5\" class=\"data row21 col5\" >-46.610000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col6\" class=\"data row21 col6\" >-46.601000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col7\" class=\"data row21 col7\" >-46.564000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col8\" class=\"data row21 col8\" >-46.479000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col9\" class=\"data row21 col9\" >-46.576000</td>\n",
       "                        <td id=\"T_7b65ba5c_b5f2_11ea_8f5a_d83bbf12c0a1row21_col10\" class=\"data row21 col10\" >-46.524000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2dd4fe26b48>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Задаем некоторые опции библиотеки pandas, которые настраивают вывод -\n",
    "# увеличиваем количество выводимых столбцов\n",
    "pd.set_option('display.max_columns', 10)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)  \n",
    "\n",
    "# применяем нашу функцию\n",
    "diagnostics_skewness(data, strategy='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Возможно в дальнейшем для некоторых переменных и некоторых моделей будет полезно \n",
    "# применить преобразования максимизирующее нормальность"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['geohash4'] = data.apply(lambda x: gh.encode(x['lat'],x['Long'], precision=4), axis = 1 )\n",
    "data['geohash5'] = data.apply(lambda x: gh.encode(x['lat'],x['Long'], precision=5), axis = 1 )\n",
    "data['geohash6'] = data.apply(lambda x: gh.encode(x['lat'],x['Long'], precision=6), axis = 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 34817 entries, 0 to 34855\n",
      "Data columns (total 24 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Rooms_Number    34817 non-null  int64  \n",
      " 1   Object_Type_ID  34817 non-null  int64  \n",
      " 2   Settlement_ID   34817 non-null  int64  \n",
      " 3   District_Id     34817 non-null  int64  \n",
      " 4   Street_Id       34817 non-null  int64  \n",
      " 5   Metro_ID        34817 non-null  int64  \n",
      " 6   Metro_m         34817 non-null  int64  \n",
      " 7   Flats_Plan_ID   34817 non-null  int64  \n",
      " 8   Stor            34817 non-null  int64  \n",
      " 9   Storeys         34817 non-null  int64  \n",
      " 10  Wall_ID         34817 non-null  int64  \n",
      " 11  Space_Total     34817 non-null  float64\n",
      " 12  Space_Living    34817 non-null  float64\n",
      " 13  Space_Kitchen   34817 non-null  float64\n",
      " 14  Value_m         34817 non-null  float64\n",
      " 15  Balcon_Num      34817 non-null  int64  \n",
      " 16  Sost_ID         34817 non-null  int64  \n",
      " 17  Clozet_ID       34817 non-null  int64  \n",
      " 18  lat             34817 non-null  float64\n",
      " 19  Long            34817 non-null  float64\n",
      " 20  Nonresidential  34817 non-null  int32  \n",
      " 21  WithoutKitchen  34817 non-null  int32  \n",
      " 22  geohash5        34817 non-null  object \n",
      " 23  geohash6        34817 non-null  object \n",
      "dtypes: float64(6), int32(2), int64(14), object(2)\n",
      "memory usage: 6.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# смотрим на типы переменных опять\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# выполняем дамми-кодирование \n",
    "# ТАК НЕЛЬЗЯ ВРОДЕ, ДО РАЗБИЕНИЯ, НО ПОТОМ РАЗБЕРУСЬ КАК СДЕЛАТЬ ПОСЛЕ РАЗБИЕНИЯ ЭТО !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
    "#data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\Cv1\\lib\\site-packages\\patsy\\constraint.py:13: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
      "  from collections import Mapping\n"
     ]
    }
   ],
   "source": [
    "#from category_encoders import TargetEncoder\n",
    "#from category_encoders import LeaveOneOutEncoder\n",
    "from category_encoders import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# создаем списки количественных\n",
    "# и категориальных столбцов\n",
    "cat_cols = data.dtypes[data.dtypes == 'object'].index\n",
    "num_cols = data.dtypes[data.dtypes != 'object'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use target encoding to encode two categorical features\n",
    "encoder = OrdinalEncoder(cols=cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms_Number</th>\n",
       "      <th>Object_Type_ID</th>\n",
       "      <th>Settlement_ID</th>\n",
       "      <th>District_Id</th>\n",
       "      <th>Street_Id</th>\n",
       "      <th>...</th>\n",
       "      <th>Long</th>\n",
       "      <th>Nonresidential</th>\n",
       "      <th>WithoutKitchen</th>\n",
       "      <th>geohash5</th>\n",
       "      <th>geohash6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1143</td>\n",
       "      <td>...</td>\n",
       "      <td>83.101</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>964</td>\n",
       "      <td>...</td>\n",
       "      <td>82.982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>...</td>\n",
       "      <td>82.980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>980</td>\n",
       "      <td>...</td>\n",
       "      <td>83.081</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>495</td>\n",
       "      <td>...</td>\n",
       "      <td>82.970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>980</td>\n",
       "      <td>...</td>\n",
       "      <td>83.077</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1835</td>\n",
       "      <td>...</td>\n",
       "      <td>83.096</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1835</td>\n",
       "      <td>...</td>\n",
       "      <td>83.100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>348</td>\n",
       "      <td>...</td>\n",
       "      <td>82.976</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>348</td>\n",
       "      <td>...</td>\n",
       "      <td>82.976</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms_Number  Object_Type_ID  Settlement_ID  District_Id  Street_Id  ...  \\\n",
       "0             1               2              1            8       1143  ...   \n",
       "1             1               3              1            1        964  ...   \n",
       "2             1               2              1            1        349  ...   \n",
       "3             2               2              1            9        980  ...   \n",
       "4             3               3              1            7        495  ...   \n",
       "5             3               2              1            9        980  ...   \n",
       "6             1               3              1            9       1835  ...   \n",
       "7             3               3              1            9       1835  ...   \n",
       "8             2               2              1            7        348  ...   \n",
       "9             2               2              1            7        348  ...   \n",
       "\n",
       "    Long  Nonresidential  WithoutKitchen  geohash5  geohash6  \n",
       "0 83.101               0               1         1         1  \n",
       "1 82.982               0               0         2         2  \n",
       "2 82.980               0               0         2         3  \n",
       "3 83.081               0               0         3         4  \n",
       "4 82.970               0               0         2         5  \n",
       "5 83.077               0               0         3         6  \n",
       "6 83.096               0               1         3         7  \n",
       "7 83.100               0               0         3         7  \n",
       "8 82.976               0               0         2         8  \n",
       "9 82.976               0               0         2         8  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = encoder.fit_transform(data)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 34817 entries, 0 to 34855\n",
      "Data columns (total 24 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Rooms_Number    34817 non-null  int64  \n",
      " 1   Object_Type_ID  34817 non-null  int64  \n",
      " 2   Settlement_ID   34817 non-null  int64  \n",
      " 3   District_Id     34817 non-null  int64  \n",
      " 4   Street_Id       34817 non-null  int64  \n",
      " 5   Metro_ID        34817 non-null  int64  \n",
      " 6   Metro_m         34817 non-null  int64  \n",
      " 7   Flats_Plan_ID   34817 non-null  int64  \n",
      " 8   Stor            34817 non-null  int64  \n",
      " 9   Storeys         34817 non-null  int64  \n",
      " 10  Wall_ID         34817 non-null  int64  \n",
      " 11  Space_Total     34817 non-null  float64\n",
      " 12  Space_Living    34817 non-null  float64\n",
      " 13  Space_Kitchen   34817 non-null  float64\n",
      " 14  Value_m         34817 non-null  float64\n",
      " 15  Balcon_Num      34817 non-null  int64  \n",
      " 16  Sost_ID         34817 non-null  int64  \n",
      " 17  Clozet_ID       34817 non-null  int64  \n",
      " 18  lat             34817 non-null  float64\n",
      " 19  Long            34817 non-null  float64\n",
      " 20  Nonresidential  34817 non-null  int32  \n",
      " 21  WithoutKitchen  34817 non-null  int32  \n",
      " 22  geohash5        34817 non-null  int32  \n",
      " 23  geohash6        34817 non-null  int32  \n",
      "dtypes: float64(6), int32(4), int64(14)\n",
      "memory usage: 6.1 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rooms_Number', 'Object_Type_ID', 'Settlement_ID', 'District_Id',\n",
       "       'Street_Id', 'Metro_ID', 'Metro_m', 'Flats_Plan_ID', 'Stor', 'Storeys',\n",
       "       'Wall_ID', 'Space_Total', 'Space_Living', 'Space_Kitchen', 'Value_m',\n",
       "       'Balcon_Num', 'Sost_ID', 'Clozet_ID', 'lat', 'Long', 'Nonresidential',\n",
       "       'WithoutKitchen', 'geohash5', 'geohash6'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# удалим их из набора данных category_encodersя 'lat','Long' :\n",
    "#data.drop(['lat','Long'],axis=1, inplace=True)\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# создаем обучающий массив признаков, тестовый массив признаков,\n",
    "# обучающий массив меток, тестовый массив меток\n",
    "train, test, y_train, y_test = train_test_split(data.drop('Value_m', axis=1), \n",
    "                                                data['Value_m'], \n",
    "                                                test_size=.3, \n",
    "                                                #stratify=data['Value_m'], \n",
    "                                                random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Преобразования максимизирующие нормальность, обработка выбросов.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Обработка редк.категорий, котор.можно выполнить только после разбиения/внутри перекр.проверки.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Импутация пропусков, котор.можно выполнить только после разбиения/внутри перекр.проверки.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rooms_Number      0\n",
      "Object_Type_ID    0\n",
      "Settlement_ID     0\n",
      "District_Id       0\n",
      "Street_Id         0\n",
      "Metro_ID          0\n",
      "Metro_m           0\n",
      "Flats_Plan_ID     0\n",
      "Stor              0\n",
      "Storeys           0\n",
      "Wall_ID           0\n",
      "Space_Total       0\n",
      "Space_Living      0\n",
      "Space_Kitchen     0\n",
      "Value_m           0\n",
      "Balcon_Num        0\n",
      "Sost_ID           0\n",
      "Clozet_ID         0\n",
      "lat               0\n",
      "Long              0\n",
      "Nonresidential    0\n",
      "WithoutKitchen    0\n",
      "geohash5          0\n",
      "geohash6          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# проверяем наличие пропусков\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Если мы заменим нулевые значения в некоторых переменных,\n",
    "то потом можно импутировать их разными методами(см.ниже):"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# выполняем импутацию пропусков в переменной Space_Total, пробуем для начала средним\n",
    "train['Space_Total'].fillna(train['Space_Total'].mean(), inplace=True)\n",
    "test['Space_Total'].fillna(train['Space_Total'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# выполняем импутацию пропусков в переменной Space_Living, пробуем для начала средним\n",
    "train['Space_Living'].fillna(train['Space_Living'].mean(), inplace=True)\n",
    "test['Space_Living'].fillna(train['Space_Living'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# выполняем импутацию пропусков в переменной Space_Kitchen, пробуем для начала средним\n",
    "train['Space_Kitchen'].fillna(train['Space_Kitchen'].mean(), inplace=True)\n",
    "test['Space_Kitchen'].fillna(train['Space_Kitchen'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4) Конструирование признаков, котор.можно выполнить только после разбиения/внутри перекр.проверки.\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# ДОБАВЛЕНИЕ ДАННОЙ ПЕРЕМЕННОЙ НЕ УЛУЧШИЛО РЕЗУЛЬТАТ(по крайне мере для леса)\n",
    "\n",
    "# создаем переменную AreaRatio\n",
    "# отношением  жилой площади к общей.\n",
    "a = 0.0001\n",
    "train['AreaRatio'] = (train['Space_Living'] + a) / ( train['Space_Total']) + a\n",
    "train['AreaRatio'].replace([np.inf, -np.inf], 0, inplace=True)\n",
    "\n",
    "test['AreaRatio'] = (test['Space_Living'] + a) / ( test['Space_Total']) + a\n",
    "test['AreaRatio'].replace([np.inf, -np.inf], 0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) Стандартизация, дамми-кодирование."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 24371 entries, 12418 to 14164\n",
      "Data columns (total 23 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Rooms_Number    24371 non-null  int64  \n",
      " 1   Object_Type_ID  24371 non-null  int64  \n",
      " 2   Settlement_ID   24371 non-null  int64  \n",
      " 3   District_Id     24371 non-null  int64  \n",
      " 4   Street_Id       24371 non-null  int64  \n",
      " 5   Metro_ID        24371 non-null  int64  \n",
      " 6   Metro_m         24371 non-null  int64  \n",
      " 7   Flats_Plan_ID   24371 non-null  int64  \n",
      " 8   Stor            24371 non-null  int64  \n",
      " 9   Storeys         24371 non-null  int64  \n",
      " 10  Wall_ID         24371 non-null  int64  \n",
      " 11  Space_Total     24371 non-null  float64\n",
      " 12  Space_Living    24371 non-null  float64\n",
      " 13  Space_Kitchen   24371 non-null  float64\n",
      " 14  Balcon_Num      24371 non-null  int64  \n",
      " 15  Sost_ID         24371 non-null  int64  \n",
      " 16  Clozet_ID       24371 non-null  int64  \n",
      " 17  lat             24371 non-null  float64\n",
      " 18  Long            24371 non-null  float64\n",
      " 19  Nonresidential  24371 non-null  int32  \n",
      " 20  WithoutKitchen  24371 non-null  int32  \n",
      " 21  geohash5        24371 non-null  int32  \n",
      " 22  geohash6        24371 non-null  int32  \n",
      "dtypes: float64(5), int32(4), int64(14)\n",
      "memory usage: 4.1 MB\n"
     ]
    }
   ],
   "source": [
    "# смотрим на типы переменных опять\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10446 entries, 18679 to 7518\n",
      "Data columns (total 23 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Rooms_Number    10446 non-null  int64  \n",
      " 1   Object_Type_ID  10446 non-null  int64  \n",
      " 2   Settlement_ID   10446 non-null  int64  \n",
      " 3   District_Id     10446 non-null  int64  \n",
      " 4   Street_Id       10446 non-null  int64  \n",
      " 5   Metro_ID        10446 non-null  int64  \n",
      " 6   Metro_m         10446 non-null  int64  \n",
      " 7   Flats_Plan_ID   10446 non-null  int64  \n",
      " 8   Stor            10446 non-null  int64  \n",
      " 9   Storeys         10446 non-null  int64  \n",
      " 10  Wall_ID         10446 non-null  int64  \n",
      " 11  Space_Total     10446 non-null  float64\n",
      " 12  Space_Living    10446 non-null  float64\n",
      " 13  Space_Kitchen   10446 non-null  float64\n",
      " 14  Balcon_Num      10446 non-null  int64  \n",
      " 15  Sost_ID         10446 non-null  int64  \n",
      " 16  Clozet_ID       10446 non-null  int64  \n",
      " 17  lat             10446 non-null  float64\n",
      " 18  Long            10446 non-null  float64\n",
      " 19  Nonresidential  10446 non-null  int32  \n",
      " 20  WithoutKitchen  10446 non-null  int32  \n",
      " 21  geohash5        10446 non-null  int32  \n",
      " 22  geohash6        10446 non-null  int32  \n",
      "dtypes: float64(5), int32(4), int64(14)\n",
      "memory usage: 1.8 MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = [col for col in train.columns if train[col].dtype.name != 'object']\n",
    "\n",
    "scaler.fit(train[num_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[num_cols] = scaler.transform(train[num_cols])\n",
    "test[num_cols] = scaler.transform(test[num_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Пока что попробуем моделирование так,\n",
    "# а потом будем постепенно улучшать все:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-квадрат на обучающем наборе: 0.33\n",
      "R-квадрат на тестовом наборе: 0.29\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression().fit(train, y_train)\n",
    "print(\"R-квадрат на обучающем наборе: {:.2f}\".format(lr.score(train, y_train)))\n",
    "print(\"R-квадрат на тестовом наборе: {:.2f}\".format(lr.score(test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-квадрат на обучающем наборе: 0.33\n",
      "R-квадрат на тестовом наборе: 0.289\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge().fit(train, y_train)\n",
    "print(\"R-квадрат на обучающем наборе: {:.2f}\".format(ridge.score(train, y_train)))\n",
    "print(\"R-квадрат на тестовом наборе: {:.3f}\".format(ridge.score(test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\Cv1\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "D:\\Anaconda3\\envs\\Cv1\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Правильность на обучающем наборе: 0.999\n",
      "Правильность на тестовом наборе: 0.431\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree = DecisionTreeRegressor().fit(train, y_train)\n",
    "tree.fit(train, y_train)\n",
    "print(\"Правильность на обучающем наборе: {:.3f}\".format(tree.score(train, y_train)))\n",
    "print(\"Правильность на тестовом наборе: {:.3f}\".format(tree.score(test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\Cv1\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-квадрат на обучающем наборе: 0.96\n",
      "R-квадрат на тестовом наборе: 0.701\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=100,random_state=100).fit(train, y_train)\n",
    "\n",
    "R2 = rf.score(test, y_test)\n",
    "print(\"R-квадрат на обучающем наборе: {:.2f}\".format(rf.score(train, y_train)))\n",
    "print(\"R-квадрат на тестовом наборе: {:.3f}\".format(R2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE на обучающей выборке: 2842.075574386538\n",
      "RMSE на тестовой выборке: 7690.695823930738\n"
     ]
    }
   ],
   "source": [
    "RMSE = np.sqrt(mean_squared_error(y_train, rf.predict(train)))\n",
    "RMSE_test = np.sqrt(mean_squared_error(y_test, rf.predict(test)))  \n",
    "\n",
    "print('RMSE на обучающей выборке:', RMSE)\n",
    "print('RMSE на тестовой выборке:', RMSE_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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